Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Authors: Fangqi Zhu
Yongqi Zhang
Lei Chen
Bing Qin
Ruifeng Xu
Venue: EMNLP 2023 Main (2023)
Keywords: Reinforcement Learning
Prompt Learning
Drug-Drug Interaction

Abstract: Adverse drug-drug interactions (DDIs) can compromise the effectiveness of concurrent drug administration, 
posing a significant challenge in healthcare. 
As the development of new drugs continues, 
the potential for unknown adverse effects resulting from DDIs becomes a growing concern. 
Traditional computational methods for DDI prediction 
may fail to capture interactions for new drugs
due to the lack of knowledge.
In this paper,
we introduce a new problem setup as zero-shot DDI prediction
that deals with the case of new drugs.
Leveraging textual information from online databases like DrugBank and PubChem, 
we propose an innovative approach TextDDI
with a language model-based DDI predictor
and a reinforcement learning~(RL)-based information selector,
enabling the selection of concise and pertinent text for accurate DDI prediction on new drugs.
Empirical results show the benefits of the proposed approach
on several settings including zero-shot and few-shot DDI prediction,
and the selected texts are semantically relevant. Our code and data are available at https://github.com/zhufq00/DDIs-Prediction.

Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Fangqi Zhu Yonggi Zhang * Lei Chen 5, Bing Qin', Ruifeng Xu 12,3
Harbin Institute of Technology, Shenzhen, China
Peng Cheng Laboratory, Shenzhen, China
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies
4Paradigm Inc., Beijing, China
Hong Kong University of Science and Technology (GZ), Guang Zhou, China zhufangqi. www.:iiiisttttmiiooooommm com, yzhangee@connecct. ust. hk, xuruifeng@hit..cm edu. cn
Abstract
Known drug-drug interactions
The new drugs
P new
Adverse drug-drug interactions (DDIs) can compromise the effectiveness of concurrent drug administration, posing significant chal- lenge healthcare. As the development of new drugs continues, the potential for unknown ad- verse effects resulting from DDIs becomes growing concern. Traditional computational methods for DDI prediction may fail to capture interactions for new drugs due the lack of knowledge. In this paper, we introduce a new problem setup as zero-shot DDI prediction that deals with the case of new drugs. Leveraging textual information from online databases like DrugBank and PubChem, we propose an in- novative approach TextDDI with language model-based DDI predictor and a reinforce- ment learning (RL)-based information selec- tor, enabling the selection of concise and per- tinent text for accurate DDI prediction on new drugs. Empirical results show the benefits of the proposed approach on several settings in- cluding zero-shot and few-shot DDI prediction, and the selected texts are semantically rele- vant. Our code and data are available at https: //gi thub. wwt.hu...oooaaaeeooomoeooeeeeeee comm/huffqooe goo P
Drug descriptions
multiple interaction types in 2
Figure I: A graphical illustration of zero-shot DDI pre- diction based on the textual descriptions. The green box in the upper left contains known interactions between a set of known drugs. The blue box in the upper right includes several new drugs that are not seen during train- ing. The task is to use drug descriptions from websites to predict interactions for the new drugs.
as computational methods, identify potential DDIs (Percha and Altman, 2013; Han et al., 2022; Margaret Savitha and Pushpa Rani. 2022).
1 Introduction
Adverse drug-drug interactions (DDIs) pose sig- nificant challenge in modern healthcare as they can alter the effectiveness of drugs when administered concurrently (Edwards and Aronson, 2000; Yan et al., 2021). With the continuous development of drugs each year, the potential for unknown adverse effects resulting from DDIs has become a growing concern, often leading to increased risks even severe heart failure. Given the constrained under- standing of these newly developed drugs and the high cost associated with conducting comprehen- sive clinical trials, there is a pressing need to ex- plore cost-effective and efficient approaches, such
The existing computational methods, which learn experience from large number of known interactions, can be classified into two categories, i.e., pure-DDI-baate based, according to the resources they use. The pure pure-DDI-based methods directly use Fingerprints of molecules (Rogers and Hahn, 2010) or learn embeddings (Yao et al., 2022) to predict the inter- action between drugs. These methods are shallow and work under the assumption that the new inter- actions have similar patterns the existing ones. KG-based methods leverage external KG (e.g. Het- ioNet (Himmelstein and Baranzini, 2015)), a large biomedical network representing the relationships between drugs and proteins, diseases, pathways,
and knowledge graph (KG)-
Work done when F. Zhu was interned 4Parudigm. CCoresspondigg authors.

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etc., to support the DDI prediction (Karim al., 2019; Vashishth al., 2020; Zitnik al., 2018; Lin et al.. 2020; Yu et al., 2021). KG embedding techniques (Zhang et al., 2022b) and graph neural networks (GNNs) (Kipf and Welling, 2016; Zhang and Yao, 2022) are commonly used in these meth- ods. Considering that not all drugs can have con- nection with the external KG, these methods may fail for drugs with rare biomedical connections.
information selector generate drug descriptions for each drug pair as its prompt. Specially, the gen- erated drug descriptions are conditioned on drug pairs to improve flexibility. By learning from the re- ward returned from the DDI predictor, the informa- tion selector effectively selects suitable sentences that align with the given pairs of drugs. The main contributions can be summarized as follows:
Problem: We propose novel problem setup that leverages textual descriptions for DDI prediction,
New drug development increasing (Atanasov et al., 2021), but predicting DDIs before clinical tests remain challenging for most computational methods due to a limited understanding of new
especially on new drugs as a zero-shot setting. Method: We carefully design an LM-based DDI predictor with an RL-based information selec- tor that accurately predicts DDIs with short and relevant descriptions.
drugs. In this paper, we study the scenario of DDI prediction new drugs, i.e., zero-shot DDI predic- tion, with limited interaction knowledge. Benefit- ing from the online databases like DrugBank and PubChem2 that provide knowledge on drugs, we can have access to many textual resources describ- ing every single drug, as graphically illustrated in Figure 1. The materials encompass several aspects of drugs, such as backgrounds, indications, and mechanism of action. Therefore, we aim to learn from these texts and capture essential information for DDI prediction the new drugs.
Experiments: We achieve better performance in zero-shot, and few-shot DDI predictions with the proposed method. In addition, we show that the selected texts by the information selector are semantically relevant to the target prediction.
Preliminaries
2.1 Problem Formulation
DDIs prediction aims to predict the interaction type given pair of drugs simultaneously used. During training (in the green box), there are several in- teractions between a set of known drugs. During inference (in the blue dashed box), we predict the interaction between new drug with known drug, or between two new drugs. Figure 1 provides a graphical illustration of the proposed problem. For- mally, define this problem as follows:
However, there come two main challenges in us- ing textural information for DDI prediction. First, the descriptions are professional with many spe- cial tokens and expressions. Therefore, the gen- eral large language models (LLMs) like ChatGPT and GPT4 are inadequate in dealing with this sce- nario. Additionally, the texts are lengthy and con- tain noise, leading to clevated computational ex- penses and heightened training complexities for LMs. To address these challenges, we design a spe- cific prompt for the DDI prediction task and fine- tune pre-trained language model to capture the domain knowledge, and then utilize reinforcement learning (RL) to describe the drugs with shorter but more relevant text for each drug pair.
Definition 1 (Zero-shot DDI prediction) Let 'D be the set of all drugs, Dnow be the set of new drugs, and I be the set of interaction types. Formally, the zero-shot DDIs prediction task is learn map- ping M : (Dnew D) (D Dnew) + I where M maps a drug pair (u, v) E ((Dnow x (D x Duew)) to the corresponding interaction type i E I.
Our approach, named TextDDI, consists of two modules: an LM-based DDI predictor and an RL- based information selector. With the specially de- signed task prompt, the LM-based DDI predictor is first trained to predict interactions based on ran- domly sampled drug descriptions, effectively cap- turing domain-speciftt knowledge. To appropri- ately describe the drugs, we design an RL-based
2.2 Background
A shift from symbolic methods The previous symbolic methods (Rogers and Hahn, 2010; Yao et al., 2022; Zitnik et al., 2018; Yu et al., 2021; 1 Lin et al., 2020; Zhang et al., 2023) rely on drug-drug interactions or the drugs' connection with other biomedical entities to encode each drug as an em- bedding. However, it lacks efficiency in encoding drugs due to the lack of knowledge of the newly developed drugs. To tackle this issuc, we shift from symbolic interactions to textual information
'ht www.drrk...ccoooo " "https://pubcher ncbi. www.mnmmm....mmmmmmm.mm.. 'https://chaa http://hhhat...ai..... and https://openai. com/gpt-4, respectively.

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describing drugs. Textual information is crucial in enabling the model to comprchend the background of new drugs, thereby facilitating accurate predic- tions of potential interactions between new drugs and other drugs.
However, RLPrompt only generates task-specific prompts, while TEMPERA is designed adjust the order or content of given inputs. Both of them cannot address the problem of selecting sentences over a long and sample-dependent raw input.
For every drug E DD with name haaee can obtain its raw descriptions nta from either the Drugbank or PubChem database. Our selection of information includes the drug's background, indi- cations, mechanism of action, etc. However, these descriptions tend to be extensive and cluttered with extrameous data. When combining the descriptive text from two drugs, the resulting text often has thousands of tokens, with only small portion be- ing actually pertinent our task. Therefore, the primary goal of our study is to extract concise and relevant text from the provided drug descriptions, and utilize it to correctly predict the DDI.
Reinforcement learning for text selection The prompt generating of drug pairs can be considered as kind of text selection problem. Within this
category, Yin et al. (2022) employed reinforcement learning for ation. They used reinforcement learning to select valuable samples from a large set of weakly la- beled sentence pairs for fine-tuning the paraphrase generation pre-trained language models. Lee and Lee (2017) utilized reinforcement learning to lect sentences for text summarization tasks, using ROUGE-2 score as the reinforcement learning's 's supervisory signal. However, both of them are unable to address the task of generating sentence sequences related to drug-drug interaction (DDI) prediction for drug pairs in the absence of existing supervision signals.
paraphrase gener-
Large language models The large language models (LLMs), such as ChatGPT and GPT4, have demonstrated remarkable proficiency across many tasks, including machine translation (Jiao et al., 2023), text summarization (Bang et al., 2023), and question answering (Tan et al., 2023), etc. De- spite their successes, ChatGPT still lags behind baseline encoder models such as RoBERTa (Liu al.. 2019) in certain specialized tasks, includ- ing affective text classification(Aminn et al., 2023) and event extraction Gao et al., 2023; Zhu et al., 2023a,b). We conducted an evaluation of ChatGPT and GPT4 in our zero-shot DDI prediction task, fol- lowing the instruction style employed by Gao et al. (2023). The results (provided in Appendix A) indi- cate that the general LLMs struggle accomplish zero-shot DDI prediction task due to their lack of relevant knowledge, suggesting the necessity of our research.
3 Method
To predict zero-shot DDI using textual information, our methodology is designed to generate shorter but more relevant prompts from lengthy and noisy drug descriptions and predict DDIs based on these generated prompts. The drug deseription we used includes information about the drug, such as its indication, mechanism of action, and more. The original description can be several thousand tokens long, the distribution of lengths is provided in Ap- pendix B, but we limit the length of the gener- ated prompt for each drug pair to only few hun- dred tokens, e.g., 256 tokens. Our method, named TextDDI, consists of two modules: LM-based DDI predictor and an RL-based information selec- tor. The DDI predictor predicts the type of drug- drug interaction based on the specially designed prompt of the drug pair, while the RL-based infor- mation selector is responsible for generating the corresponding prompt based on the drug descrip- tions of the drug pair. Additionally, we alternate the training pipeline of these two modules to facili- tate their mutual enhancement. Figure illustrates an overview of our method in the inference stage.
Prompt learning Recent studies suggest that LMs can efficiently conduct new tasks through a few in-context demonstrations (Brown et al., 2020), but their performance is significantly influenced by the formulation of the prompt (Zhao et al., 2021; Lu et al., 2022). To address this sensitivity, sev- eral methods for automatic prompt generation have been proposed. In the continuous space, Lester et al. (2021) introduces prompt-tuning which adds train- able tokens as the prompt for each task improve performance. In the discrete space, RIPrompt (Deng et al., 2022) and TEMPERA (Zhang et al., 2022a) propose to use RL to adjust the prompt.
3.1 DDI Predictor
The DDI predictor is designed to accurately predict DDIs by utilizing prompts generated from drug de-

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(plu, v)) (r).predin
Probability distribution P(llp(u,v] of interaction type i E J.
Language Model based DDI Predictor
prompt Pr u,
[CSS
Drug
Drug description
Drug name:
Drug description
Instruction
[SEP]
e.g.
CLS
Allopunioo Allopunnol a xanthine oxidase inhibitor... Caloffedio:: Calcifediol an active metabolite of vitamin.... the above context. we can predict the drug-drug interaction between Allopurina and Calcifadio is that: SEP
Information Selector
1,2, ... until length(p, v)) I.
DO
(b). previous
sampee Senlence My
construuct prompt
prompt Pr P
based on probability Eq,(4)
Sentence Nt,
Sentence
hamee Allopurinol
uname
Calcifediol
Given (a).get descriptions (u, 2)
p
Figure 2: The inference procedure of the proposed TextDDI. (a). Given a drug pair, we obtain the drug descriptions from websites. (b). The previous prompt Pr- [u, v] (where polu, v] is "[CLS] umame
yoome () [Task
Instruction] [SEP]") is fed into the selector. (c). The information selector samples one sentence from descriptions based on their priority score. (d). The prompt Pe [u, oj is constructed with Pt-1 [u, u] and the new sentence uy- (e).
When the prompt Pt (u, v| exceeds the length limit L, we stop the loop and use P:- [l,, v) as the final prompt p to predict the interaction type of drug pair (u, v).
scriptions. As the prompts are important down- stream tasks (Zhao al., 2021; I; Lu et al., 2022), design prompt for the DDI prediction task here. As graphically illustrated in the top part of Fig- ure 2, the prompt contains names, and descriptions given drug pairs (u, v). To guide the model to do DDI prediction, the task instruction is given as in the blue content. Hence, the prompt is designed to indicate the task, and describes the information of both drugs to be predicted.
type i E I between and The classification model is based on pre-trained language model LMp(-), whose input plu, e] and the output em- bedding of the final hidden state of the [CLS] token passed through multi-class linear classifier predict the interaction type. For a certain example (u, i, v) E Stra. the classification model returns the probability P(ilplu, ul), and the DDI predictor is trained by minimizing the cross-entropy loss:
Cp Σ log (P(ilplu,
(1)
However, considering that the drugs' description can have thousands of tokens, while the complexity of the pre-trained LM increases quadratically in terms of sequence length, we cannot use all the descriptions. Instead, we randomly select some sentences and put them into the prompts. For given drug pair (u, v), we have their names name and name and we randomly select sentences sel and sel from the descriptions raw and faw respectively. Afterwards, we create the prompt plu, v]. constrained with maximum length limit of L. by combining [CLS], u name, scl name ydel the task instruction, and [SEP].
(u,i,))SS
3.2 Information Selector
To create succinct and relevant descriptions that serve as prompts for drug pairs, straightforward approach would be to annotate these descriptions and then train a model to select them, but this re- quires costly biomedical experts. In addition, there is no simple heuristic to indicate such relevance. Therefore, we propose learning method that dis- covers which kind of information is helpful for DDI prediction. Since the sentences are discrete, design an innovative reinforcement learning (RL) framework in this part to automatically gen- crate drug-pain deirddependent prompts, with the DDI predictor serving as the supervisory signal.
After constructing the prompt plu, v). we pro- ceed to train a classification model, referred to as the DDI predictor, to predict the interaction

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Reinforcement learning formulation. Consid- ering the sentences describing drugs have some correlation with each other, we formulate the prompt generation process as a Markov Deci- sion Process (MDP) (Bellman, 1957). 4-tuple (S,A,P, R) is defined to represent the MDP, where S is the state space, A contains all avail- able actions, P is the policy designed to select actions, and R. is the reward function based on the performance of DDI prediction.
Subsequently, Multilayer Perceptron (MLP) is employed to evaluate the score for each possible action, denoted by
s s(a) = 10:-MMPPPP (LM. 11,, r[u,,l))) ul)),
where is the concatenation operation. We then apply the softmax function derive the probability distribution over these actions a E Ar, i.e.
P(alpi-i(u,, = cxp(s(a))
Ed'EA exp(s(a')) (4)
State space S. Forming an MDP, the prompt is generated sequentially to select the relevant infor- mation related to DDI prediction. Denote Pa (u, u) the prompt generated at step i, we directly use Pelu, v] as the state. Recall in Section 3.1, the prompt is composed of drug-related information, the task descriptions, and some auxiliary tokens. Specifically, we have
and sample sentence at ~ P(alp:-1 Di o)) at step t. a₁ is inserted into P:- (2, v) to form pr pi o), denoted as pe [u, e] == Pt [u, v] O az
Reward R.. The key to reward design is ensur- ing to accurately reflect the gain from Pi-i(u [u, v] to Pe(ut u). Following RLPrompt (Deng al., 2022), we measure the quality of current prompt prlu, v| by the difference between the probabil- ity of the correct interaction type and the prob- ability of the most probable incorrect type, de- noted as: Pilu, uI) == log Pil u]) max log P(i' P(il|p:[uu (u, u)). Based on d(i, Pelu, vl), the i'
S pelu, u] [[CLS] u name uh
name of [Task instruction] [SEP].
(2)
At the initial step f. = O, we have ugd = "0" = 0. And at each time step we select one sentence from either raw or Faw and adds it into - or ofd respectively. If the length of pi pilu, vj sur- passes the predefined maximum limit L, the pro- cess will be halted and Pu-1(u, u, u] will be returned as the final prompt plu, w).
quality score defined as
Prlu, u)) - x cdii
where C = [d(i,p) > 0] indicating whether the interaction type i is correctly predicted, and À1 A2 > 0 are weights for the correct and incorrect predictions, respectively. In general, the quality score is positive when the prediction is correct, oth- erwise, it is negative. Then, the difference between the quality scores of pr(u, w) and P-11(u, ul is used to measure the reward of action a,
Action space Are To generate a prompt based on the description of drug pair, include the sentence sequences faw and naw from the raw de- scription for drug pair (u, v) in our action space. In order to avoid the selection of repeated sentences, we take out one sentence from the non non-sclected ones. Consequently, our action space
R₂ q(t,prlu, v)) g(i. Pt ((, u)). (5)
A 2 U Un Faw
sel U o
(3)
gradually narrows down step increases.
3.3 Training Pipeline
Policy P(a+ E Adpp- v)). Given the prompt Pu-1(u, u]. how to select the next sentence a, from action space A₃ is the key component in our de- sign. We utilize the weights of LMp in DDI predictor to initialize an identical encoder L.M The new encoder LM. is used to encode the sen- tences in A, and measure their plausibility given prompt [u, v). At step L, we encode the prompt [u,
Our training pipeline detailed in Algorithm 1. To capture the domain knowledge, we generate ran- dom prompts for drug pairs in the training set and minimize (1. to fine-tune the DDI predictor in line 2. The weight of LMp in DDI predictor is also used to initialize the weights of LM, in informa- tion selector in line 2. Subsequently, we repeatedly use the information selector generate prompt and alternatively update the policy network as well the DDI predictor in lines 3-15. For each sample (u, 2, in the train set Sia. we iteratively select
Pt [u, 2] by taking the output embedding of the [CLS] token LM (pt-1(u, u)) as its representation. We also encode (M) for each sentence a E Ar.

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Algorithm 1 Training Pipeline of TeXIDDI.
number of interactions as S ((,,,,)) u, D, E Σ}. Based on the drug split, the train, valid, test sets are given as
Require: DDI predictor, information selector,
training set Sun. validation set Syat-
1: fine-tune DDI predictor with randomly gener- ated prompts in Sua by minimizing (I);
Sma = {(u, v) E S u,u5 Duai:
Swal = {(u,i,)) E S : (u E Dua U A (v
2: initialize the weights of LMę by LMp:
Dua U Dvai) A iu) & Sma::
S = {(,,,, E 8 (u D D (v
3: repeat
4: for (u, v) E Sun (in mini-batch) do
Draa (Da) (u, Sma).
5:
+ and set prompt polu, 2];
In general, we ensure that the new drugs remain invisible during training. The statistics of the two zero-shot datasets are provided in Table 1.
6: repeat
7:
sample a₂ P(a E AllPi-1lu, u));
8:
pr(u, u] Pt- Pt- v| @ axi compute reward R₂ with (5);
For the DrugBank dataset, we utilized text de- scriptions from the Backgrounds, Indications, and Mechanism of Action sections. For the TWO- SIDES dataset, we used text descriptions from the Description section. For both datasets, we did not use the Drug Interaction section to prevent infor- mation leakage.
9:
10:
t t +1 1;
11:
until length(p:[u, [u, ul) > L:
12:
update parameters of information selector by maximizing (6);
13:
end for
14:
update DDI predictor using the prompts gen- erated by information selector in Sim until performance on Sval is stable; return DDI predictor, information selector.
Table I: The statistics for both datasets used for the zero-shot setting.
Datasets
[D]
[Z]
|Sma
[Sail
[Swl
DrugBank
1,710
86
129,522
29,402
26,240 5,712
and formulate drug-pair prompt until the length exceeds in lines 6-11. The internal rewards col- lected in line 9 are used to update the information selector by maximizing the expectation
TWOSIDES
604
200
30,832
3,962
4.1.2 Baselines
We compare the proposed method with three kinds of baseline methods. First, the pure-DDI based methods that direct predict given (u, v), including MLP (Rogers and Hahn, 2010) that predicts DDIs based on drugs' fingerprints (molecular- related fcatures);
J E Σ
( Ea--ioon-
(6)
where y < is the discount factor, in line 12 in a mini-batch manner. In line 14, we update the DDI predictor with the prompts generated by informa- tion selector such that it can be improved by the prompts with more relevant information. When the evaluated performance validation set Syal no longer gets improved, we stop the loop and return the DDI predictor and the information selector.
MSTE (Yao et al., 2022) that is KG embedding technique predict DDI by learning drug's and interaction's 's embeddings.
Second, the KG-based methods that leverage exter- nal KG to predict the interaction, including
KG-DDI (Karim et al., 2019) that uses KG em- bedding technique to learn embeddings over both the DDI interactions and the external KG;
4 Experiments
4.1
Experimental Setup
CompGCN - (Vashishth et al., 2020), Decagon (Zitnik et al., 2018) and KGNN (Lin et al., 2020) that design variants of GCN to aggregate drugs' representations from externl KG for DDI prediction;
4.1.1
Datasets split for zero-shot DDI
Following (Zimikk et al., 2018; Yu et al., 2021), we conduct experiments on two benchmark datasets: DrugBank (Wishart et al., 2018) and TWO- SIDES (Tatonetti et al., 2012). To construct the dataset for zero-shot DDIs prediction, we divided the set of drugs, D, into three disjoint sets, D Dval and Dest, in chronological order based on the date the drugs are developed. Denote the total
SumGNN (Yu et al., 2021) that extracts subgraph covering given drug pair from the KG and pre- diets interaction based the subgraph.
Third, the text- text-based methods that use textural in- formation, including a DDI predictor with only

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Table 2: Comparison of different methods on the zero-shot DDI prediction task. The reported results include the average and standard deviation across five distinct random seeds. For all the metrics, the larger values indicate better performance. The best values are in boldface and the second best are underlined.
Dalasets
DrugBank
TWOSIDES
Type
Methods
F1-Score
Accuracy
Kappa
PR-AUC
ROC-AUC
Accuracy
Pure-DDI
MLP (Rogers and Hahn, 2010)
28.7
44.940.9
31.7
71.0
73.1
67.
MSTE (Yao al., 2022)
9.4203
45.520.2
34.2+0.11
64.6g0.1
69.9
63.4
KG-based
KG-DDI (Karim et al., 2019)
23.5_0.1
48, 48.820.9
37.9
73.640.3
75.8
67.8102
CompGCN (Vashishth ct al., 2020)
30.
49.840.
40. 40.1
72.940.4
81.640.6
75.1
Decagon (Zitmik et al., 2018)
32.9=1.2
53.4408
43.4+07
79.5+12
83.9+0.9
75.941.1
KGNN (Lin et al., 2020)
28.920.9
48 13.33
38.641.1
75.4+0.9
74.8+1.0
65. 65.2+08
SumGNN (Yu et al., 2021)
19.1
47.210.9
36.810.7
74.010.8
76.411.
70.0
Text-hased w/ drug id drug name
21.1 10.5
46.910.7
35.3
67.410.4
72.910.3
67.1 10.5
42.1=0.1
62.1+0.3
54.54003
72.3+0.2
78.940.3
75.2+03
w/ iruncated descriptions
48.010.6
65.8
58.610.4
84.210.3
85.311.4
77.210.6
w/o information selector
48.8
65.2
58.110.3
85.310.3
85.610.4
78.310.5
TextDDI
52.5+0.7
67.3+n.4
60.5-0.4
88. 28.2
88, 88.420.
80.3
(relative improvement)
19.6%
13.9%
17.1%
8.7%
4.5%
4.4%
drug ids (w/ drug id), DDI predictor with only drug names (w/ drug name), DDI predictor with truncated description (w/ truncated description) and a DDI predictor with randomly generated prompts (wlo information selector). For all the variants, task instructions are provided.
4.1.4 Implementation details
Unless otherwise specified, for the language model, we utilize RoBERTa-Baae -asee (Liu et al., 2019) with a maximum prompt length limit of L 256, along with the Adam optimizer (Kingma and Ba, 2014). The performance of TexDDI with different LMs is provoided in Appendix F. In Algorithm 1, for both the DrugBank and TWOSIDES datasets, the DDI predictor and information selector undergo two training rounds until achieving stable perfor- mance on S,al- Each training process indepen- dently converges the respective modules. To con- trol the variance, the well-known Proximal Policy Optimization (PPO) (Schulman et al., 2017) algo- rithm used maximize (6) All experiments are conducted on a server equipped with 8 NVIDIA GeForce RTX 3090 GPUs. More details regarding hyperparameters are provided in Appendix D.
4.1.3 Metrics
For the DrugBank dataset, which involves multi- class classification task, we're using three key metries: macro F1-Score, Accuracy, and Cohen's Kappa (Cohen, 1960). The F1-Score is calculated as the mean of the harmonic mean of precision and recall for each interaction type. Accuracy cal- culates the correct prediction rate, while Cohen's s Kappa assesses the accuracy beyond chance agree- ment. The TWOSIDES dataset requires multi-label predictions with each interaction type considered as binary classification problem. Metries used in- clude ROC-AUC (Fawcett, 2006), PR-AUC (Saito and Rehmsmeier, 2015), and Accuracy. ROC-AUC and PR-AUC gauge the performance of binary clas- sification by analyzing true positives and false pos- itives, and precision and recall respectively. Due to the imbalanced occurrence of DDI types, chose the F1-Score as primary metric for the Drugbank dataset and PR-AUC for the TWOSIDES dataset because simultaneously considers precision and recall. More details the metrics are provided in Appendix C.
4.2 Zero-shot DDI Prediction
From the comparison in Table 2, we have the fol- lowing notable observations: (i) Among the Pure- DDI methods, MSTE performs worse than MLP. This shortfall is due to MSTE's KG embedding technique which cannot update the embeddings of new drugs, which has no interaction training, owing to the lack of DDIs involving new drugs. (ii) Among the KG-based methods, KG-DDI uses the KG embedding technique as well but outperforms MSTE. This superior performance is achieved by updating the embeddings of new

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4.3 Few-shot DDI Prediction
60
In Section 4.2, we studied the case of no interaction on new drugs. However in practice, there can be some known knowledge on the newly developed drugs. Hence, in this part, we compare the differ- ent models' abilitics in a few-shot setting, where there can be a few interactions for the new drugs. Specifically, we move 1, 3 and 5 samples (names as 1, 3 and 5 shot, respectively) of each new drug into the training set to setup this scenario.
50
Fl score (9))
40
30
MSTE
20
Decagon
SUMGNN
10
TextDDI
0
1
3
Number of shots
We compare our method with several typical baselines, including Pure-DDI methods (MLP. MSTE) and KG-based methods (Decagon, SumGNN) on the DrugBank dataset. The results are shown in Figure 3. We can observe that the performance of all methods improves the number of shots increases. Notably, MSTE exhibits the most significant improvement due its heavy dependency on known DDIs to predict DDIs for each drug. KG-based approaches such as Decagon and SumGNN exhibit comparable performance growth, attributable to their similar GNN architecture. Once again, in the few-shot setting, our method still outperforms the baselines significantly, as our model can efficiently learn from the DDIs of new drugs.
Figure 3: Comparison with baselines under the different number of shots of DDIs seen during training.
drugs using entities in the biomedical KG. Regard- ing deep GNN-based methods such as CompGCN, Decagon, KGNN, and SumGNN, their perfor- mance is remarkably similar. This is mainly at- tributed to their common use of Graph Neural Net- work (GNN) architecture for updating drug embed- dings, along with their application of connections between the new drug and entities in the biomed- ical KG to update the embedding of the drug. In general, the deep GNN- methods outperform the shallow ones like KG-DDI, MSTE, and MLP.
We also evaluate the performance of TexIDDI in the vanilla setting in Yu et al. (2021) without specially caring about the new drugs. The experi- mental results are provided in the Appendix E.
(iii) Among the Text-based methods, it is evi- dent our TextDDI outperforms previous baselines by a significant margin. It is noteworthy that just using the drug name (w/ drug name) already sur- passes most of the baselines based on graph meth- ods. We found this is because the naming of drugs generally follows specific patterns. For instance, statin class drugs are used for lowering blood pressure, and drugs like Lovastatin (DB00227) and Fluvastatin (DB01095) have the same suffix. When convert the drug names to drug IDs (e.g., DB00227), the model's performance (w/ drug id) significantly declines, proving that valuable infor- mation is also contained in the drug names. At the same time, we observed that using the trun- cated drug descriptions as prompts (w/ truncated description) and randomly generated prompts (w/o information selector) to predict DDIs have simi- lar performance. When the information selector is removed, either w/ truncated description or w/o information selector, the model's performance de- clines, indicating that the information selector mod- ule can enhance the DDI predictor's performance by generating more accurate prompts.
4.4 Case Study on the Selected Information
In this section, we conduct a case study to visu- alize the prompt generated by the information se- lector. From the examples presented in Table 3, we observe the huge distinction between the ran- dom prompt and the learned prompt. The infor- mation in the randomly generated prompt is very diverse and it fails predict the correct interaction type. On the other hand, the prompt generated by the information selector provides comprehensive descriptions of Aclidinium and Butylscopolamine as "anticholinergic" drugs that act through distinct mechanisms of action. The drug descriptions (with several "anticholinergic" and "muscarinic" related messages) provided by the information selector sist the DDI predictor in accurately predicting the DDI type, i.e.. "Aclidinium may increase the anti- cholinergic activities of Butylscopolamine". This indicates that our method possesses a certain de- gree of interpretability.

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Table Comparison between the randomly generated prompt and the prompt generated by the information selector. Red and green highlight incorrect and correct predictions by the DDI predictor, while orange marks DDI type-related keywords, Refer to Appendix G for more cases.
nor prolong the QTc interval or significant effects cardine rhythm. Aclidinium indicated for the long temm, maintenance treatment of bronchospasm associated with chronic obstructive pulmonary disease (COPD), including much higher propensily
Aclidinium:
inhalation powder
Randomly geverated prompt
chronic bronchitis and emptyseema,
receptors than nicotinic receptors. bronchovenstricion elfects was dese-dependent and lasted longer than 24 hours. Butyboooolamiie: receptors the gestrointstiiall tacet. The inhibition
approved July
24, 2012. Proveation of scctylchotine-indnee
Used abdaminnal cramping and pain. Scopolamine buttihrmmide hinds of contraction reduces spasms and their related pain during abdominal cramping. In the above context, we can predict that the drug drug interaction between Aclidinium and Bugysooooammine is that: Aclidinium decrease the bronchodilstory activities of Buty hcoplooco..
Aclidinium: Prevention of sesylchliix-indueee bro.bocomstiction effects dose- dose-dependnt and lasted longer than 24 bours. Aclidinium long-acting, competirive. and reversible anticholineeric drag speaific
Prompt generated
accty sholine mascarinic receptors. It to musesrinic receptor sutrypes similar affinity Aclidinlum's 's effects the airways are medinted through the M3 receptor the smooth musele to eaose branchodilation. Butyscopolamine: smmolth muscle. Scopolamine butylbooidde binds to muscarinic MB receptors in the gastrointestinal tract. The inhibition of contraction roduces spasms and their related pain during ahdomina cramping. In ahove context, and Butyssogolamine is that: Aclidinium may increase the anticholinergic aclivilies of Buty lccppolaane
by the formation
prevents acetyccholine
hinding to activating receptors
contraction
prodict that the ding--ring internction hetween Aclidinium
Square root of GPU hours
8
Performance
4-
50
Kappa
2-
45
128
256
512
64
128
256 Maximum Length
512
Maximum Length
(a) Performance comparison.
(b) Computational costs.
Figure 4: The impact of maximum prompt length on model performance, and computational costs.
4.5 Ablation Study: Length of Prompt
Conclusion
In this section, we conduct ablation experiments on the DrugBank dataset to investigate the impact of prompt length on performance and computational costs. As shown in Figure 4a, when the length of the prompt increases from 64 to 128 and 256, the model's performance significantly improves. However, when the length further increases to 512, the performance improvement becomes negligible, possibly due decrease in the density of task- relevant information within the longer prompt. As depicted in Figure 4b, can be observed that the square root of the GPU hours necessary for achiev- ing model convergence exhibits a linear correla- tion with the maximum length of the prompt. This finding supports the quadratic association between prompt length and computational costs. Therefore, shortening the length of the prompt is necessary. Thanks our information selector's ability to gen- erate shorter and more relevant text, we can en- hance model performance while conserving com- putational costs.
We propose a problem setup that predicts DDI for new drugs as zero-shot setting. To achieve this, we propose TextDD1, a novel framework that capitalizes on textual descriptions of each single drug. TextDDI consists of an LM-based DDI pre- dictor and an RL-based information selector. The LM-based DDI predictor trained with drug-pair- dependent descriptions to capture Nomain-specifii knowledge. The RL-based information selector is designed to generate shorter but more relevant de- scriptions for each drug pair and is trained with DDI predictor serving the supervisory signal. Experimental results demonstrate that TextDDI achieves better DDI prediction performance than the existing symbolic approaches under zero-shot and few-shot settings. We also show that the de- scriptions generated by the information selector are semantically relevant to the target prediction.

---

Limitations
Ji. Tiezheng Yu, Willy Chung, et al. 2023. A multi- task, multilingual, multimodal evaluation of chatgpt reasoning, hallucination, and interactivity. Tech- nical report, arXiv:2302.04023.
Our proposed TeXtDDI for zero-shot DDI predic- tion still contains some limitations. Te may fail when lacking descriptions of drugs, such as the background, indications, and mechanisms of action. The greedy search used in our information selector can be replaced with beam search stralegy, which may lead better performance. Due to computa- tion resources, the largest model we use is limited to GPT-2 XL (1.5B, in Appendix F). We believe that TextDDI has great potential for improvement in future works.
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Ethics Statement
We follow the ACL Code of Ethics. In our work. there are no human subjects and informed consent is not applicable.
EMNLP, pages 3369-3391.
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Acknowledgments
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Fangqi Zhu, Bing Qin and Ruifeng Xu's works were partially supported by the Na- tional Natural Science Foundation of China 62176076), Shenzhen Foundational Research Fund- ing JCYJ202208181024155000 cial Key Laboratory of Novel Security Intelligence Technologies 2022B 0111122100000 1000000 Lei Chen's work was partially supported by National Science Foun- dation of China (U22B2060, 61729201), the Hong Kong RGC GRF Project 162 (32236,0, CRF Project C2004-21 GF, RIF Project R6020-19, AOE Project AoE/E-603/18, theme-based project TRS T41- 603/20R, Guangdong Basic and Applied Basic Re- search Foundation 2019B151530001, Hong Kong ITC ITF grants MHX/078/21 and PRP/004/22FX Microsoft Rescarch Asia Collaborative Research Grant and HKUST-Webank joint research lab grants.
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A GPT's For the Zero-shot DDI Prediction
B Distribution of Entire Prompt Lengths
We present the distribution of prompt lengths for each drug pair using complete drug descriptions in Figure 5. The maximum length for text drug descriptions from websites 4081. The maximum length for the descriptions of drug pairs is 6575. As discussed in Section 4.5, the computa- tional costs increase quadratically with the length of the prompt. Consequently, training a fine-tuning model with prompts consisting of several thousand tokens becomes costly. Therefore, reducing the length of the prompt is necessary, which demon- strates the necessity of our information selector.
In this section, we evaluate the performance of the state-of-the-ar general large language models, including GPT-o.5-Turbo (the API version of Chat- GPT) and GPT4, the zero-shot DDI prediction task.
The task instruction template we utilized follows the style employed by Gao et al. (2023), which in- cludes task description, interaction types and their definitions, positive example and negative example. Please refer Table 9 for details about task instruction template. We randomly generated prompts evaluate the performance of the large language models.
20000
Due to the request limitations of GPT4, the eval- uated dataset we utilized consists of 100 randomly selected samples from the zero-shot DDI predic- tion Stst on the DrugBank dataset. For the predictions that cannot be exactly matched with the
15000
Frequency
10000
definition of DDI types, we select the DDI type
5000
by calculating the similarity between the predic- tion text and the definition of DDI types. We also attempt to have GPT-4 use a browser plugin to an- swer questions. However, in this scenario, GPT-4 unable to follow instructions and predict DDI types. The results of our evaluation are shown in Table 4.
0
2000
4000
6000
Length
Figure 5: Distribution of prompt length for complete
drug-pair prompts constructed with raw drug descrip- tions.
Table Comparison of TextDDI, GPT-3.5-Turbo. and GPT4 on the zero-shot DDI prediction task. The Miss Rate refers to the proportion of instances that fails to follow the instruction predict exact definition of interaction type.
C Metric Details
In this section, provide more detailed descrip- tion of the metries.
Methods
F1-Score
Accuracy
Kappa
Miss Rate
For the DrugBank dataset, since there is at most one interaction between a drug pair, the task is a multi-class class classification problem. Therefore, we consider the following metrics:
Random Guess
0.0
0.0
-0.4
GPT-3.5-Tubo
0.9
2.2
1.3
7%
GPT4
6.6
10.0
8.1
4%
TexIDDI
35.2
55.0
45.2
F1-Score & Ererp P.+R. where P. and R2 the precision and recall for the interaction type r, respectively.
2P,-R,
From Table 4, we observe that the performance of GPT-3.5-Turbo is only marginally better than random guessing, primarily due to its lack of do- main knowledge. GPT-4, on the other hand, demon- strates remarkably enhanced capabilities compared to GPT-3.5-Tiue especially in complex and spe- cialized tasks such zero-shot DDI prediction. However, neither of both large language models, GPT-3.5-Turbo or GPT-4, can achieve the zero- shot DDI prediction task as effectively as TexIDDI, underscoring the significance of our research.
Accuracy: the percentage of accurately pre- dicted interaction type compared with the ground-truth interaction type.
Cohen's Kappa (Cohen, 1960): x P.-P.
1-P. where P. is the observed agreement (accu- racy) and Pe is the chance agreement (the probability of randomly seeing each class). For the TWOSIDES dataset, since there may be multiple interactions between drug pair, the task is a multi-labs prediction problem, where each

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Table Comparison different methods on the vanilla DDIs prediction task, which predicts interactions between two known drugs. Our results include the average and standard deviation across five distinct random seeds. The best values are in boldface and the second best are underlined.
Datasets
DrugBank
TWOSIDES
Type
Methods
F1-Score
Accuracy
Kappa
PR-AUC
ROC-AUC
Accuracy
Pure-DDI
MLP (Rogers and Hahn, 2010)
61.1=0.4
82.1
80.5
82.6=0.a
81.240.1
73.5+0.3
MSTE (Yao et al., 2022)
83.0=1.s
85.4
82.8+0.s
90.2-o.1
91.3+0.1
84.1+0.1
KG-based
KG-DDI (Karim et al., 2019)
52.2-1.1
61 B1.822.
55.9+2.8
88 88.2-0.1
90.7+0.1
83.5+a.1
CompGCN (Vashishth al., 2020)
74.3_1.2
78.8.0.8
75. 75.0+111
90.6=0.3
92.3+0.3
84.8+0.3
Decagon (Zitnik et al., 2018)
57.4-0.3
27.3
86.1+a.1
90.6=0.a
91.7++..3
82.1+a.5
KGNN (Lin et al., 2020)
74.0_o.1
90.9=0.2
89.6 20.62
90.8_0.2
92.840.1
86.1
SumGNN (Yu et al., 2021)
86.9_o.4
92.720.1
90.710.1
93.4_o.1
91.910.2
88.810.2
Text-based
TexIDDI
91.7
95. 95.4
94.1
95.2 95.2=0.4
89.0+a.a
type of interaction is treated as a binary classifi- cation problem. Following (Zitnik et al., 2018; Tatonetti et al., 2012), we sample one negative drug pair and assess the performance of binary classifi- cation with the following metries:
Table 6: Hyperparameters used for TextDDI across all settings.
Hyperparameter Value
Learning rate Weight Decay
0.00001
ROC-AUC the area under curve of re- ceiver operating characteristics, measured by E TP:4PPP where (T, FP)) the k-th true positive and false positive operating point.
0.000006
Batch Size
128
Discount Factor
0.99
GAE Lambda
0.95
PPO Epoch
PR-AUC is the area under curve of precision- recall, measured by Ek= P&A Rạ where (Px, R&) is the k-th precision and recall oper- ating point.
Number of Mini Batch
4
Clip Ratio
0.1
Value loss Coefficient
0.5
Entropy Coefficient
0.01
Accuracy is the percentage of accurately pre- dicted interaction type compared with the ground-truth interaction type.
that TexDDI still achieves significant lead us- ing the vanilla setting. We also analyze the reason why TextDDI' lead on the TWOSIDES dataset is not as pronounced as on the DrugBank dataset. This may be due to the lower quality of textual information for drugs in the TWOSIDES dataset. Some drugs are named using organic compound nomenclature, such as Saarrboxy---ydroxyy-MM trimethhlproppn---amiinn -arrppaann--mmiimm.. In such cases, more data cleaning work required to replace organic compound names with common drug names.
Training Details
We provide more training details in this sec- tion. We use generalized Advantage Estima- tion (GAE) to smooth the rewards. All the hy- perparameters are listed in Table 6. For fur- ther information on the PPO algorithm, please refer to https://sppimnnngup.. openai. com/en/ latest/algori wwwpslppppttttoopooooooo html.
E Vanilla DDI Prediction
F Comparison of Different Language Models
In this section, we conduct experiments using the vanilla setting, which has been commonly em- ployed in previous studies. This setting does not involve any specific handling of new drugs, and it ensures that all drugs are evenly distributed across the training, validation, and test sets. The dataset partitioning described Yu al. (2021) is utilized for this purpose. The corresponding results are pre- sented in Table 5. We can observe from Table
In this section, we evaluate the performance of TextDDI using different language models, in- cluding encoder models (RoBERTa-Base and RoBERTa-Large), well decoder models scale to different sizes (GPT2, GPT2-Medium, GPT2- Large, and GPT2-XL). For the decoder model, we utilize the output embedding of the final hidden

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Table Performance comparison different language
models on the zero-shot DrugBank dataset.
LM
Parameters Num.
FI-Se Score
Accuracy
Kappa
RoBERT> Base
125M
52.5
67.3
60.5
RoBERTa-Large
355M
51.4
66.1
58.6
GPT2
124M
47.4
65.2
57.6
Mer2-Medium
355M
GPT2-
774M
56.6
64.3
57.0
GPT XL.
1500M
61.4
64.8
57.0
state of the final decoder token "
" as the repre- sentation of input to finetune the model.
Referring to Table 7, we can observe that larger language models generally yield superior results, with our primary focus being the F1-Score for the DrugBank dataset. For all language models, the hyperparameters are the same shown in Table 6. Additionally, we observe that the performance of the RoBERTa-Laage model unsatisfactory com- pared to RoBERTa-Base, indicating a requirement for further hyperparameter optimization. And We find that the model size increases in the GPT2 series models, the FI-Score significantly improves, while the accuracy slightly decreases. This indi- cates that model performance outcomes can vary based on the evaluation dimension. Overall, larger models tend to achieve better performance. For example, in terms of F1-Score, GPT-XI. exhibits 14% improvement compared to GPT2.
G More Case Studies
this section, we presented more examples as shown Table 8.

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Table 8: Further comparison between the randomly generated prompts and the prompts generated by the information selector. The text highlighted in red and green represents the portions of the DDI predictor's predictions that are incorrect and correct, respectively. The orange portion represents keywords related to the correct DDI type.
Bupivacaine: As implant, bupivacaine is indicated in adults placement into the surgical site to produce postsurgical analgesia for up to 24 hours following open inguinal hermia repair. Rupivacaine, in combination with meloxicam, is indicated for postsurgical analgesia adult patients for up to 72 hours following foot and ankle. -mall-to-medium open abdominal. and lower extremity total joint arthroplasty surgical procedures. Bupivacaine is often administered by spinal injection prior total hip urthroplasty. Verapamil: Verapamil is indicated in the treatment of vasopastic (i.e. Prinzmetal's s) angina, unstable angina, and chronic stable angina. Verapamil binds to these channels in voltage- and anginaa manner, meaning affinity is increased 1) as vascular smooth muscle membrane potential is reduced, and 2) with excessive depolarizing stimulus. In the above context, we can predict that the drug-drup interaction between Bupivacaine and Verapamil is that: The risk or severity of adverse effects can be increased when Bupivacaine is combined with Verapamil.
Randomly generated prompt
frequency-depender
Case
Bupivacaine: Like, bupivacaine is an amide local anesthetic that provides local anesthesia through blockade of nerve impulse generation and conduction. Bupivacaine crosses the neuronal membrane and exerts its anesthetic action through blockade of these channels at the intracellular portion of their pore- forming tranamem segments. These impulses, also known as action potentials, critically depend on membrane depolarization produced by the influx of sodium ions into the neuron through voltage-gate gated sodium channels. The block is use-dependent, where repetitive or prolonged depolarization increases sodium channel hlockade. Verapamil: Verapamil is known to interact with other targets, including other calcium channels, potassium channels, and adrenergic receptors. Verapamil binds to these channels in voltage- and
Prompt gen- crated by the information selector
manner, meaning affinity is increased I) as vascular smooth musele membrane potential is reduced, and 2) with excessive depolarizing stimulus. N-, P-, Q-, or T-type). In the above context, we can predict that the drug-drug interactions between Bupivacaine and Verapamil is that: The metabolism of Verapamil can be decreased when combined with Bupivacaine.
Penbutolol: Penbutolol is B-1, 3-2 (nonsclective) adrenergic receptor antagonist. In human studies, however, heart rate decreases have been similar to those seen with propranolol. When 31 receptors are activated by catecholamines, they stimulate coupled G protein that leads to the conversion of adenosine triphosphate (ATP) cyclic adenosine monophosphate (cAMP). The increase in cAMP leads to activation of protein kinase A (PKA), which alters the movement of calcium ions in heart muscle and increases the heart rate. Teriflunomide: Teriflunomide is pyrimidinc synthesis inhibitor with unti-inflammtorry and immunomodulatorry properties used to treat patients with the elaasing-remitting form of multiple sclerosis. The FDA label states an important warning about the risk of hepatoxicity and teratogenicity for patients using teriflunomide. the above context, can predict that the drug interactions between Penhutolol and Teriflunomide that: The metabolism of Teriflunomide can he decreased when combined with Penbutolol.
Randomly generated prompt
Case 2
Penbutolol: Penbutolol acts on the 3-1 adrenergic receptors in both the heart and the kidney. When 3-1 receptors are activated by catecholamines, they stimulate coupled protein that leads to conversion of adenosine triphosphate (ATP) to eyclic adenosine monophosphate (cAMP). Penbutolol blocks the catecholamine activation of 81 adrenergic receptors and decreases heart rate, which lowers blood pressure. The increase cAMP leads to activation of protein kinase A (PKA), which alters the movement of calcium ions in heart muscle and increases the heart rate. Teriflunomide: What is known is that teriftunomide prevents pyrimidine synthesis by inhibiting the mitochondrial enzyme dihydroorotate dehydrogenase, and this may be involved in its inmunomodulatory effect in MS. The exact mechanism by which teriflunomide acts in MS is not known. In the above context, we can predict that the drug-drug interactions between Penbutolol and Teriflunomide is that: Penbutolol may decrease the antihypertensive activities of Teriflunomide.
Prompt gen- crated by the
information selector

---

Table 9: The task instruction template we utilze, where the bold text represents the part that needs to be predicted by the large language model. Please note that the instruction is drug- prug-pair-dependent and the names umeme and unume, as well as the descriptions sel and yoo! the drug pair (u, v) will be filled into the template during the prediction process.
Task Instruction
Task Description: This a drug-drug interactions prediction task where the goal is to predict the interaction type given a pair of drugs.
Interaction types and their definitions:
#Drugl may increase the photosensitizing activities of #Drug2. #Drugl may increase the anticholinergic activities of #Drug2. The bioavailability of #Drug2 can be decreased when combined with #Drugl.
Positive Example:
Input: #Drugl: Riboflavin, #Drug2: Verteporfin.
Context:
Riboflavin: The antioxidant activity of rihoflavin is principally derived from its role a precursor of FAD and the role of this cofactor in the production of the antioxidant reduced glutathione. Reduced glutathione is the cofactor of the selenium- containing glutathione peroxidases among other things. The glutathione peroxidases are major antioxidant enzymes. Reduced glutathione is generated by the FAD-containing enzyme glutathione reductase. Binds to riboflavin hydrogenase, riboflavin kinase, and riboflavin synthase.
Verteporfin: Verteporfin appears somewhat hat preferentially accumulate in neovasculature. including choroidal neovasculature. Verteporfin is transported in the plasma primarily by lipoproteins. However, animal models indicate that the drug is also present in the retina. As singlet oxygen and reactive oxygen radicals are cytotoxic, Verteporfin can also be used to destroy tumor cells.
Output: #Drugl may increase the photosensitizing activities of #Drug2.
Negative Example:
Input: #Drugl: Riboflavin, #Drug2: Verteporfin.
Context:
Riboflavin: The antioxidant activity of riboflavin is principally derived from its role a precursor of FAD and the role of this cofactor in the production of the antioxidant reduced glutathione. Reduced glutathione the cofactor of the selenium- containing glutathione peroxidases among other things. The glutathione peroxidases major antioxidant enzymes. Reduced glutathione generated by the FAD-containing enzyme glutathione reductase. Binds riboflavin hydrogenase, riboflavin kinase, and rihoflavin synthase.
Verteporfin: Verteporfin uppears sotewhat preferentially accumulate in neovasculature, including choroidal ncovasculature. Verteportin is transported in the plasma primarily by lipoproteins. However, animal models indicate that the drug is also present the retina. singlet oxygen and reactive oxygen radicals are cytotoxic, Verteporfin can also be used destroy tumor cells.
Output: #Drugl may decrease the vasoconstricting activities #Drug2.
Evaluation Instance:
Input: #Drugl: [u"ame) #Drug2: (umame).
Context:
(urume }: [u
Qusetion: What is your guess for the interaction type between #Drugl: [u*mme), #Drug2: (v"m))) answer one of the above interaction types. does not have be fully correct. Output: The risk severity heart failure be increased when #Drug2 combined with #Drugl.


Reviews:
[{"review_id": "DylytwrnnQ", "reviewer": "Reviewer_yZ3w", "summary": "", "questions": "Q1. From where do you obtain the drug description, and do you verify whether there is a data leakage problem?\nQ2. Why is the input length set so short? Is there a compelling reason for this, or is it merely for the convenience of the experiment?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: *The construction of the dataset is unclear.*\n\n**A1**: Thank you for your suggestions. \n\nIn #line 388 ~ 391, we introduced that we are using standard datasets, DrugBank and TWOSIDES, following Zitnik et al. (2018) and Yu et al. (2021). In #line 156 ~ 168, we described the source of our drug description information. And Section 4.4.1 *Datasets split for zero-shot DDI* details how we partitioned the dataset for zero-shot DDI.\n\nWe will organize these information and incorporate additional information in Section 4.4.1 *Datasets split for zero-shot DDI* in future versions as follows:\n\n- For the DrugBank dataset, Ryu et al. (2018) downloaded 192,303 DDIs described in the form of sentences from the [DrugBank database](https://go.drugbank.com/releases) (version 5.0.3), **Drug Interactions** Section. DDI types that occurred less than 5 times were filtered out, resulting in a total of 86 DDI types and 185, 164 DDIs. For each drug, we utilized information from the [DrugBank website](https://go.drugbank.com/drugs/DB00001) concerning the drugs' *Backgrounds*, *Indications*, and *Mechanisms of action* as the original drug descriptions.\n- For the TWOSIDE dataset, Tatonetti et al. (2012) collected the dataset from large collections of adverse event reports, considering only high-confidence DDIs. For each drug, we use the drug *Description* section on the [PubChem website]((https://pubchem.ncbi.nlm.nih.gov/compound/5206)) as the original description of the drug.\n\n[1] Marinka Zitnik, Monica Agrawal, and Jure Leskovec. 2018. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13):i457–i466.\n\n[2] Yue Yu, Kexin Huang, Chao Zhang, Lucas M Glass, Jimeng Sun, and Cao Xiao. 2021. Sumgnn: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics, 37(18):2988–2995.\n\n[3] Ryu,J.Y. et al. (2018) Deep learning improves prediction of drug–drug and drug–food interactions. Proc. Natl. Acad. Sci. USA, 115, E4304–4311.\n\n[4] Tatonetti,N.P. et al. (2012) Data-driven prediction of drug effects and interactions. Sci. Transl. Med., 4, 125ra31.\n\n\n\n**Q2**: *It's highly likely that there's a **data leakage problem**. It is reasonable to include the textual description of known drugs, but this description likely contains information related to drug-drug interaction outcomes. For instance, on Wikipedia, there's a specialized **interaction section** that discusses potential interactions with other chemicals. **Question**: From where do you obtain the drug description, and do you verify whether there is a data leakage problem?*\n\n**A2**: Thank you for your reminder, but **we can confirm that there is no data leakage problem** here because we have already taken this factor into account when obtaining drug descriptions.\n\n-  For the DrugBank dataset, the drug descriptions we utilized include information from the *Backgrounds*, *Indications*, and *Mechanism of action sections* (#line 076 ~ 077) on the [DrugBank website](https://go.drugbank.com/drugs/DB00001), **excluding the Drug Interaction section**. The Backgrounds, Indications, and Mechanism of action sections primarily provide information about the properties of the indexed drugs without including interactions with other drugs.\n\n-  For the TWOSIDES dataset, the drug descriptions we used are from the *Description* section of each drug on the [PubChem website](https://pubchem.ncbi.nlm.nih.gov/compound/5206) , which provides basic information about the indexed drugs. Similarly, there is a separate *Interactions* section on the PubChem website, which we **did not use**.\n\nReviewers can browse our provided [DrugBank website](https://go.drugbank.com/drugs/DB00001) and [PubChem website](https://pubchem.ncbi.nlm.nih.gov/compound/5206) to further ensure that there are no data leakage issues here.\n\nThank you for your concern regarding this issue. We will include descriptions to highlight the exclusion of data leakage problem in future versions.\n\n\n\n**Q3**: *The improvement brought about by the RL-based information selector may be attributed to the relatively short input length limitation. The authors have set a limit for the input length to 256 tokens, meaning that each drug description can only be 128 tokens long, roughly amounting to 100 words. I believe this is insufficient to encapsulate all necessary information about a drug, making the selection of information valuable. However, if more input is permitted, would the RL selector contribute equally? Especially in today's context, most of the state-of-the-art (SOTA) Large Language Models (LLMs) support input with more than a thousand tokens. I am unsure if this setting is reasonable. **Question**: Why is the input length set so short? Is there a compelling reason for this, or is it merely for the convenience of the experiment?*\n\n**A3**: \nIn regard to why the input length is set to 256, our explanation is as follows:\n\nIn Section 4.5 *Ablation Study: Length of Prompt*, we analyzed the effect of input length on both the model's performance and its computational costs. Specifically, in #line 562 ~ 565, we stated：\"However, when the length further increases to 512, the performance improvement becomes negligible, possibly due to a decrease in the density of task-relevant information within the longer prompt\".\n\nIn fact, the input length of 256 was determined through hyperparameter tuning. Since we were using the pretrained language model RoBERTa with a maximum input length of 512, we experimented with setting the input length to 64, 128, 256, and 512, as outlined in Figure 4. Ultimately, we found that when training with a length of 512, the model's performance only marginally improved upon 256, while the computational cost increased quadratically. On the DrugBank dataset for example, the performance of F1-score 52.5 at $L=256$ required 23 GPU hours, whereas the F1-score 52.9 at $L=512$ required 80 GPU hours.\n\n**Due to limited computing resources (only 8 NVIDIA GeForce RTX 3090 GPUs) and to reduce the cost of subsequent experiments, we chose a maximum length of 256, with only a slight loss in performance but still outperforming all the baselines.** For fair comparation, we choose $L=256$ to conduct the various subsequent experiments, including the *Few-shot settings* (Section 4.3), *vanilla DDI prediction experiments* (Appendix E), and *Comparison of Different Language Models experiments* (Appendix F). Especially, the GPT-XL experiment required 312 GPU hours at a maximum length of 256, thus setting the maximum length to 512 in this case would be hard to afford, estimated to require more than one week for training.\n\nRegarding the question of whether the information selector can make the same contribution on longer input lengths, we present the ablation experiments of TextDDI on the information selector when the input length is 512 as follows:\n\nFor the DrugBank dataset:\n\n| Method                                         | F1-Score | Accuracy | Kappa |\n|------------------------------------------------|----------|----------|-------|\n| w/o information selector (input length=256)    | 48.8     | 65.2     | 58.1  |\n| TextDDI (input length=256)                     | 52.5 (+3.7)     | 67.3 (+2.1)     | 60.5 (+2.4)  |\n| w/o information selector (input length=512)    | 49.3     | 65.5     | 59.4  |\n| TextDDI (input length=512)                     | 52.9 (+3.6)    | 67.9 (+2.4)    | 61.4 (+2.0) |\n\nFor the TWOSIDES dataset:\n| Method                                         | PR-AUC | ROC-AUC | Accuracy |\n|------------------------------------------------|----------|----------|-------|\n| w/o information selector (input length=256)    | 85.3     | 85.6     | 78.3  |\n| TextDDI (input length=256)                     | 88.2 (+2.9)    | 88.4 (+2.8)    | 80.3 (+2.0) |\n| w/o information selector (input length=512)    | 85.6     | 85.4     | 78.1  |\n| TextDDI (input length=512)                     | 88.7  (+3.1)   | 88.2 (+2.8)   | 80.7 (+2.6) |\n\n**The additional experimental results *w/o information selector (input length=512)* demonstrate that the information selector continues to provide nearly the same improvement(2.0 ~ 3.6%) on both datasets when the input length is 512. This further validates the contribution of the information selector.**\n\n\n\n\n\n**Q4**: *Most language models the authors utilize are far smaller than the current so-called LLMs. I would suggest not using the term large language models.*\n\n**A4**: \nThank you very much for this advice, and we will change \"large language models (LLMs)\" to \"pretrained language models (PLMs)\".", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The authors propose an innovative approach. The TextDDI approach, which combines Language Models with Reinforcement Learning (RL) for information selection, is a novel and creative solution to the problem. \n\n2. Good Relevance to Real-world Challenges. Adverse DDIs are a critical concern in healthcare, and the ability to predict such interactions, especially for new drugs, has immediate practical implications. Thus, the research addresses a timely and impactful real-world issue.", "reasons_to_reject": "1. The construction of the dataset is unclear, and it's highly likely that there's a data leakage problem. It is reasonable to include the textual description of known drugs, but this description likely contains information related to drug-drug interaction outcomes. For instance, on Wikipedia, there's a specialized interaction section that discusses potential interactions with other chemicals. See Q1.\n\n2. The improvement brought about by the RL-based information selector may be attributed to the relatively short input length limitation. The authors have set a limit for the input length to 256 tokens, meaning that each drug description can only be 128 tokens long, roughly amounting to 100 words. I believe this is insufficient to encapsulate all necessary information about a drug, making the selection of information valuable. However, if more input is permitted, would the RL selector contribute equally? Especially in today's context, most of the state-of-the-art (SOTA) Large Language Models (LLMs) support input with more than a thousand tokens. I am unsure if this setting is reasonable. See Q2.", "questions_to_authors": "Q1. From where do you obtain the drug description, and do you verify whether there is a data leakage problem?\nQ2. Why is the input length set so short? Is there a compelling reason for this, or is it merely for the convenience of the experiment?", "paper_topic_and_main_contributions": "This paper addresses the critical challenge in healthcare regarding the prediction of adverse drug-drug interactions (DDIs) through utilizing pre-trained language models and textual knowledge.\n\nMain Contributions:\n1. The authors present an innovative approach named TextDDI, which leverages textual information from online databases like DrugBank and PubChem to predict potential drug-drug interactions.\n\n2. The authors propose a novel Reinforcement Learning (RL)-based Information Selector to select the most concise and relevant text for improving the performance of DDI prediction.\n\n3. The authors have made the code and data used in this study available at the provided URL, contributing to the research community's ability to replicate and extend the study.", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "S1. Most language models the authors utilize are far smaller than the current so-called LLMs. I would suggest not using the term large language models."}, "extra_text": {}}, {"review_id": "ltjmS64896", "reviewer": "Reviewer_Na97", "summary": "", "questions": "Question A: Would the performance be as high as reported for truly new drugs? When would this method fail?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: *(Minor) The search for relevant information seems greedy. I imagine there are cases where the greedy addition of information can go haywire, resulting in incorrect predictions. It would be useful to see cases when this happens. I assume using beam search or some other method of optimizing the sequence at inference time could help, but it may not completely mitigate the issue.*\n\n**A1**: \nThank you for your feedback. In general, the advantage of greedy search over beam search is that it has **lower computational overhead** and is **simpler to implement**. However, the downside of greedy search is that it tends to get stuck in local optima and generates sequences that lack diversity. For example, when using greedy search for natural language generation, it often produces repetitive words. TextDDI generates short but more relevant drug descriptions at the sentence level based on the raw description of drug pairs, but the selection of sentences is done without replacement (#line 312 ~ 316), which *avoids the problem of generating duplicate sentences when using greedy search*. Compared to greedy search, beam search has higher complexity because it requires interactions with the DDI predictor every time to obtain the probability of a sentence based on Eq. (4). \n\nSince our method's primary objective is to select the most informative and relevant information, maintaining sentence coherence through beam search is not as crucial. Furthermore, the search strategy is not our main contribution and greedy search has already achieved good performance. \n\nWe appreciate your suggestion of taking beam search into consideration. In the conclusion section, we will mention that beam search or its variant strategies could be considered for future work.\n\n\n\n**Q2**: *(Minor) This approach assumes a large description of the new Drug in a very specific style. However, for real \"new\" drugs, this description may be unavailable or unreliable/incomplete. The current evaluation pipeline assumes that these new descriptions are easily accessible, available, and informative. So, the zero-shot results may be overestimated in practice. A solution to this \"cold-start\" issue may be required for accurate performance. I think the limitations section could be expanded on these additional points. Note: This is probably an issue with prior work as well.*\n\n**A2**: **Drug-drug interactions (DDIs) are typically assessed during the latter stages of drug development.** The process of developing a new drug generally comprises several phases, such as basic research, preclinical studies, and clinical trials. The information required for TextDDI is primarily provided during the basic research and preclinical studies stages, including the drug's background, indications, and mechanism of action. Clinical trials, on the other hand, primarily focus on the safety and efficacy of the drug, including drug-drug interactions. **Therefore, when predicting drug-drug interactions for a new drug, it is highly likely that researchers already have access to the relevant drug descriptions.**\n\nIt's worth noting that many of the baseline methods require Drug-Drug Interaction (DDI) information between the new drug and other drugs, or the linkage of new drugs to the Biomedical Knowledge Graph, in order to understand new drugs. Obtaining such information is more challenging than acquiring the drug descriptions used in our method.\n\nFollowing your suggestion, we will add the following additional limitation regarding TextDDI in future versions:\n- TextDDI requires drug background, indications, and mechanism of action as descriptive information to help the model understand new drugs, which might limit its application for really new drugs in the early stages of drug development.\n\n\n\n\n**Q3**: *(Very Minor) The use of **kappa** scores seem a bit out-of-place. Instead of using kappa scores to evaluate if the method is better than random, the proposed could be compared to random, and similar, baselines (e.g., predicting at random, predicting the most common class all the time, etc.), which would be a bit more informative.*\n\n**A3**: We consider using the Kappa metric for two reasons:\n\n1. Kappa is a classic evaluation metric, particularly useful when dealing with imbalanced classification categories like the DDIs in our problem.\n2. As previous baselines like SumGNN used the kappa metric, we also include kappa as a fair and more comprehensive comparison for different methods.\n\nThe concept behind the kappa metric is to measure the difference between the accuracy of a classifier and the accuracy it would achieve through random guessing:\n\n$\\kappa = \\frac {{P_0 - P_E}} {{1 - P_E}} $\n\nwhere $P_0$ represents the classifier's accuracy, and $P_E$ describes the accuracy of the classifier making random guesses.\n\nExamining the performance difference between a classifier and one that always predicts the most common class is quite interesting. Similarly, we can define a new kappa as follows:\n\n$\\kappa' = \\frac {{P_0 - P_C}} {{1 - P_C}} $\n\nwhere $P_0$ is the classifier's accuracy, and $P_C$ describes the accuracy of a classifier that predicts the most common class.\n\nTaking into account $P_C$=0.290, we can calculate the new kappa metric performance (%) for the 10 methods in Table 2 as follows:\n\n22.4(MLP), 23.2(MSTE), 27.9(KG-DDI), 29.3(CompGCN), 34.4(Decagon), 27.2(KGNN), 25.6(SumGNN), 46.6(w/o description), 51.0(w/o information selector), 53.9(TextDDI)\n\nWe found that the new kappa, $\\kappa'$, is linearly correlated with accuracy. Kappa and $\\kappa'$ have maintained almost the same trend, except for KG-DDI and CompGCN. Additionally, all baseline models outperform predictions of the most common class (Accuracy=29%). We will consider adding this metric in future versions as you suggested.\n\n\n\n**Q4**: *Would the performance be as high as reported for truly new drugs? When would this method fail?*\n\n**A4**: The years of the drugs in the test set we used range from 2021 to 2022, while the drugs in the validation set span from 2017 to 2020. The training set covers the years from 1907 to 2017. **Hence, the experiment setup in our paper is trying to resemble the case of truly new drugs.**\n\nIn practice, we are only available to some public dataset and the truly new drugs may only be known by the drug developers. **Hence, we may not be able to conduct this kind of experiments.**\n\nSince our method has not been applied in practice yet, its performance in predicting Drug-Drug Interactions (DDIs) with truly new drugs is currently uncertain. **One of the limitations of our TextDDI is that it will fail without descriptions of the background, indications, and mechanisms of action of new drugs. (When would this method fail?)**\n\nHowever, as discussed in A2, we consider safety verification, which includes DDI, to be a later stage in the development of new drugs. Therefore, we believe that the necessary drug description will be obtainable at this stage. Previous methods required harder-to-obtain information for predictions, such as DDIs with other drugs for new drugs, and manual updates to the biomedical knowledge graph for new drugs. In comparison, our method is more amenable to practical application.\n\n\n\n**Q5**: *Missing References: We acknowledge that there is substantial research on using reinforcement learning for text selection beyond prompt learning. For example, substantial research has been conducted using reinforcement learning for summarization and paraphrasing. Expanding on the related work section in this area could be useful.*\n\nREFERENCES: Yin, Haiyan, Dingcheng Li, and Ping Li. \"Learning to selectively learn for weakly supervised paraphrase generation with model-based reinforcement learning.\" Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.\n\nLee, Gyoung Ho, and Kong Joo Lee. \"Automatic text summarization using reinforcement learning with embedding features.\" Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2017.*\n\n**A5**: Thank you for your suggestion. We will add a paragraph on **reinforcement learning for text selection** in the *Background* section in future versions. The discussions are provided as follows:\n\n**Reinforcement learning for text selection**\nThe prompt generating of drug pairs can be considered as a kind of text selection problem. Within this category, Yin et al. (2022) employed reinforcement learning for weakly-supervised paraphrase generation. They used reinforcement learning to select valuable samples from a large set of weakly labeled sentence pairs for fine-tuning the paraphrase generation pre-trained language models. Lee et al. (2017) utilized reinforcement learning to select sentences for text summarization tasks, using ROUGE-2 score as the reinforcement learning's supervisory signal. However, both of them are unable to address the task of generating sentence sequences related to drug-drug interaction (DDI) prediction for drug pairs in the absence of existing supervision signals.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Overall, the experiments in this paper are very well done, with averages reported across random seeds, detailed descriptions of the training, development, and test datasets, computational cost experiments, and a useful case study depicting why the method works.\n\nI really liked the idea of the information selector. The approach could be relevant beyond the descriptions in DrugBank, e.g., PubMed research articles. Ultimately, the approach seems generalizable.", "reasons_to_reject": "\n(Minor) The search for relevant information seems greedy. I imagine there are cases where the greedy addition of information can go haywire, resulting in incorrect predictions. It would be useful to see cases when this happens. I assume using beam search or some other method of optimizing the sequence at inference time could help, but it may not completely mitigate the issue.\n\n(Minor) This approach assumes a large description of the new Drug in a very specific style. However, for real \"new\" drugs, this description may be unavailable or unreliable/incomplete. The current evaluation pipeline assumes that these new descriptions are easily accessible, available, and informative. So, the zero-shot results may be overestimated in practice. A solution to this \"cold-start\" issue may be required for accurate performance. I think the limitations section could be expanded on these additional points. Note: This is probably an issue with prior work as well.\n\n(Very Minor) The use of kappa scores seems a bit out-of-place. Instead of using kappa scores o evaluate if the method is better than random, the proposed could be compared to random, and similar, baselines (e.g., predicting at random, predicting the most common class all the time, etc.), which would be a bit more informative.\n", "questions_to_authors": "Question A: Would the performance be as high as reported for truly new drugs? When would this method fail?", "paper_topic_and_main_contributions": "This paper introduces a novel method of predicting drug-drug interactions (DDI) using drug descriptions with a particular emphasis on zero-shot prediction (i.e., drugs that have not been seen in the training dataset). The method works using two components: a DDI predictor and an information selector. The DDI predictor is trained using random snippets from the descriptions of two drugs as input. The information selector uses reinforcement learning to identify the sentences in the description that maximizes model performance. The model performance of the proposed method substantially outperforms prior work.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "There is substantial research on using reinforcement learning for text selection beyond prompt learning. For example, substantial research has been conducted using reinforcement learning for summarization and paraphrasing. Expanding on the related work section in this area could be useful.\n\n\nREFERENCES:\nYin, Haiyan, Dingcheng Li, and Ping Li. \"Learning to selectively learn for weakly supervised paraphrase generation with model-based reinforcement learning.\" Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.\n\nLee, Gyoung Ho, and Kong Joo Lee. \"Automatic text summarization using reinforcement learning with embedding features.\" Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2017."}}, {"review_id": "fHXFYpfhi3", "reviewer": "Reviewer_mJfs", "summary": "", "questions": "1. What is the maximum length of the text drug descriptions from websites? What is the proportion of texts that exceed the model's maximum input length?\n2. The results of three metrics (i.e., F1-scores, Accuracy and Kappa) show inconsistent trends. Which metrics do you prefer to be the main metric?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: *The reason why the proposed method outperforms other baselines is unclear. From Table 2, the proposed method without descriptions also achieves promising results on two datasets and even outperforms all baselines on the DrugBank dataset. The proposed method without descriptions means that only the text of drug names is used for DDI predictions. Why can the model using only drug name text outperform other baselines using more drug information? The authors should analyze and explain this.*\n\n**A1**: \nThank you for your reminder. \n\nDuring the experimental process, we have considered why the performance of the DDI predictor using only drug names almost outperforms all baselines. In the supplementary materials, our submitted code's configuration file contains a `use_id` parameter, indicating that only the drug's ID, such as DB00001 is used for prediction as a futher ablation for drug names(*w/o description*). The experimental results for drug id only are as follows:\n\nFor the DrugBank dataset:\n\n| Method         | F1-Score | Accuracy | Kappa |\n| -------------- | -------- | -------- | ----- |\n| drug id only   | 21.1     | 46.9     | 35.3  |\n| drug name only | 42.1     | 62.1     | 54.5  |\n| TextDDI        | 52.5     | 67.3     | 60.5  |\n\nFor the TWOSIDES dataset:\n\n| Method                     | PR-AUC   | ROC-AUC  | Accuracy |\n|----------------------------|----------|----------|----------|\n| drug id only               | 67.4     | 72.9     | 67.1     |\n| drug name only             | 72.3     | 78.9     | 75.2     |\n| TextDDI                    | 88.2     | 88.4     | 80.3     |\n\nWe can observe that, when drug names are replaced with drug IDs without using other descriptions, the performance of the DDI predictor significantly decreases, performing only slightly better than MSTE among the seven baselines on the both datasets. \n\nThe experimental results demonstrate that **drug names may also contain valuable information**, as the nomenclature of drugs typically follows certain conventions. These names are often simple and descriptive, making it easier for professionals to identify the category and function of the drugs. For instance, statin drugs like Lovastatin (DB00227) and Fluvastatin (DB01095) are both used to lower cholesterol and prevent cardiovascular diseases. This similarity could assist models in predicting on unseen statin drugs such as Simvastatin (DB00641) and Atorvastatin (DB01076).\nAnother illustrative example of informative drug naming involves proton pump inhibitors like Omeprazole (DB00338), Lansoprazole (DB00448), and Pantoprazole (DB00213). They are all used to reduce gastric acid secretion and treat gastric ulcers, and they share the common ending \"-prazole.\"\n\nWe have also found some examples of similar DDIs (drug-drug interactions) for Lovastatin and Simvastatin with the same drugs, such as Abatacept and Acalabrutinib:\n- The metabolism of Lovastatin can be increased when combined with Abatacept.\n- The metabolism of Simvastatin can be increased when combined with Abatacept.\n- The metabolism of Lovastatin can be decreased when combined with Acalabrutinib.\n- The metabolism of Simvastatin can be decreased when combined with Acalabrutinib.\n\n**These examples can help us explain how the model makes zero-shot DDI predictions solely based on drug names.**\n\nIn summary, we attribute the strong performance of the DDI predictor that only uses drug names for prediction(*w/o description*) to: 1. Drug names being highly condensed representations of drug information and 2. The powerful capabilities of pre-trained models. We will include this analysis in Section 4.2 *Zero-shot DDI Prediction* of future versions.\n\n\n\n\n**Q2**: *The proposed DDI predictor using all textural information (Truncate if the text is over max length) should be added for comparison.*\n\n**A2**: Thank you for your suggestion again. We share the same idea, and we have actually implemented this baseline (textDDI with truncated description) before. \nIn the supplementary materials, our submitted code's configuration file contains a `not_random` parameter, which corresponds to the implementation of the truncated description baseline.\n\n**However, this baseline performs similarly to predicting with randomly selected sentences (*w/o information selector*), as shown in the table below. Hence, we did not discuss it in the current version considering the space constraints.** We will discuss this baseline in future versions.\n\nFor the DrugBank dataset:\n\n| Method                     | F1-Score | Accuracy | Kappa |\n|----------------------------|----------|----------|-------|\n| truncated description      | 48.0     | 65.8     | 58.6  |\n| w/o information selector   | 48.8     | 65.2     | 58.1  |\n\nFor the TWOSIDES dataset:\n\n| Method                     | PR-AUC   | ROC-AUC  | Accuracy |\n|----------------------------|----------|----------|----------|\n| truncated description      | 84.2     | 85.3     | 77.2     |\n| w/o information selector   | 85.3     | 85.6     | 78.3     |\n\n**Q3**: *What is the maximum length of the text drug descriptions from websites? What is the proportion of texts that exceed the model's maximum input length?*\n\n**A3**: \n\nThe maximum length for text drug descriptions from websites is 4081.\nThe maximum length for the raw descriptions of drug pairs is 6575, as indicated in Figure 5 of Appendix B.\n\nAppendix B displays the length distribution of drug descriptions(for drug pairs). Among them\n\n- 99% of drug pairs have raw drug description lengths exceeding our set maximum input length of 256.\n- 98% of drug pairs have raw drug description lengths exceeding the maximum length of the RoBERTa model, which is 512.\n- 63% of drug pairs have raw drug description lengths exceeding the maximum length of the GPT-2 model, which is 1024. \n\nFor the description of a single drug: \n\n- 85% of drugs have raw drug description lengths exceeding our set maximum input length of 256.\n- 42% of drugs have raw drug description lengths exceeding the maximum length of the RoBERTa model, which is 512.\n- 12% of drugs have raw drug description lengths exceeding the maximum length of the GPT-2 model, which is 1024. \n\nWe will add the above information in Appendix B in future versions.\n\n**Q4**: *The results of three metrics (i.e., F1-scores, Accuracy and Kappa) show inconsistent trends. Which metrics do you prefer to be the main metric?*\n\n**A4**: **In #line 910, we mentioned that we adopted the F1-Score as our main metric.** We chose the F1-Score because it assigns equal weight to each DDI type. Due to the imbalanced occurrence of DDIs, for instance, the most frequently occurring DDI type among 86 DDI types in the DrugBank dataset accounts for nearly 29%, using F1-Score as the main evaluation metric is fairer.\n\nTo further investigate this inconsistency trend, we assigned numbers to ten models as follows: MLP (0), MSTE (1), KG_DDI (2), CompGCN (3), Decagon (4), KGNN (5), SumGNN (6), w/o description (7), w/o information selector (8), and TextDDI (9). The rankings of these models based on three metrics (from lowest to highest) are as follows:\n\nF1-Score: 1 2 0 5 6 3 4 7 8 9\n\nAccuracy: 0 1 6 5 2 3 4 7 8 9\n\nKappa   : 0 1 6 2 5 3 4 7 8 9\n\nWe observed that the inconsistency between metrics decreases when the model performance is better, and the top-performing five models exhibit the same trend (i.e., 3 4 7 8 9). Additionally, we found that the trend in Accuracy closely aligns with Kappa, possibly due to Kappa being derived from Accuracy.\n\n**For the TWOSIEDS dataset, for the same reasons, we chose PR-AUC as the main metric because it simultaneously considers precision and recall.**\n\nWe will clarify this in future versions.\n\n\n\n\n**Q5**: *The description of algorithm 1 is not standardized. The algorithm should have inputs and outputs.*\n\n**A5**: Thank you for your suggestion. In Algorithm 1, \"`Require`\" indicates the input of the algorithm, which includes the DDI predictor, the information selector before training, the training set $S_{tra}$ , and the validation set $S_{val}$ . TextDDI iteratively performs supervised fine-tuning on the DDI predictor and uses reinforcement learning to train the information selector, supervised by the DDI predictor. In the end, \"`return`\" term represents the output of the algorithm, including DDI predictor and information selector after training. In future versions, we will standardize the algorithm by replacing \"require\" with \"inputs\" and adding \"outputs\" row below, while removing the \"return\" row.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The authors propose a novel problem setup that leverages textual descriptions for zero-shot DDI prediction for new drugs.\n2. The proposed TextDDI applies the LLM for the DDI predictor and reinforcement learning to further improve the performance. The experimental results on two datasets show that the proposed model outperforms all baselines.", "reasons_to_reject": "1. The reason why the proposed method outperforms other baselines is unclear. From Table 2, the proposed method without descriptions also achieves promising results on two datasets and even outperforms all baselines on the DrugBank dataset. The proposed method without descriptions means that only the text of drug names is used for DDI predictions. Why can the model using only drug name text outperform other baselines using more drug information? The authors should analyze and explain this.\n2. The proposed DDI predictor using all textural information (Truncate if the text is over max length) should be added for comparison.", "questions_to_authors": "1. What is the maximum length of the text drug descriptions from websites? What is the proportion of texts that exceed the model's maximum input length?\n2. The results of three metrics (i.e., F1-scores, Accuracy and Kappa) show inconsistent trends. Which metrics do you prefer to be the main metric?", "paper_topic_and_main_contributions": "In this paper, the authors focus on zero-shot drug-drug interaction (DDI) prediction for new drugs. A new approach TextDDI is proposed to leverage textual information. The proposed model consists of a large language model (LLM)-based DDI predictor and a reinforcement learning (RL)-based information selector. The DDI prediction uses a LLM model to capture the domain knowledge, and then the information selector utilizes RL to describe the drugs with shorter but more relevant text for each drug pair. The experimental results on the DrugBank and TWOSIDES datasets show that the proposed model outperforms all baselines.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "The description of algorithm 1 is not standardized. The algorithm should have inputs and outputs."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper investigates a LLM-based approach for predicting drug-drug  interactions (DDI). The key idea of the proposed model is to mutually learn adequate prompts that leverage descriptive information from external resources, in the one hand, and DDI prediction score using a reinforcement learning policy in the other hand. 

The reviewers agree that the overall contribution is interesting and innovative, presenting a new research angle on a well-known prediction task for health, namely DDI.
Even if the evaluation part looks reasonable, there have been some concerns about the limited input length, as well as the lack of appropriate ablation study scenarios to fully support the soundness of the results (e.g., DDI predictor / and w/ textual features such as name, truncated vs. full description). 

Overall, the discussion phase among the reviewers expressed a positive feeling about the paper and its potential and even its generalizability to other tasks.
Decision: Accept-Main