Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023)

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.

## introduction adverse drug-drug interactions (ddis) pose a significant challenge in modern healthcare as they can alter the effectiveness of drugs when administered concurrently @xcite . 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 or even severe heart failure. given the constrained understanding of these newly developed drugs and the high cost associated with conducting comprehensive clinical trials, there is a pressing need to explore cost-effective and efficient approaches, such as computational methods, to identify potential ddis @xcite @xcite . the existing computational methods, which learn experience from a large number of known interactions, can be classified into two categories, i.e., pure-ddi-based and knowledge graph (kg)based, according to the resources they use. the pure-ddi-based methods directly use fingerprints of molecules @xcite or learn embeddings @xcite to predict the interaction between drugs. these methods are shallow and work under the assumption that the new interactions have similar patterns as 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, etc., to support the ddi prediction @xcite @xcite @xcite . kg embedding techniques @xcite and graph neural networks (gnns) @xcite are commonly used in these methods. considering that not all drugs can have a connection with the external kg, these methods may fail for drugs with rare biomedical connections. new drug development is increasing @xcite , but predicting ddis before clinical tests remain challenging for most computational methods due to a limited understanding of new drugs. in this paper, we study the scenario of ddi prediction on new drugs, i.e., zero-shot ddi prediction, with limited interaction knowledge. benefiting from the online databases like drugbank foot_0 and pubchem foot_1 that provide knowledge on drugs, we can have access to many textual resources describing 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 on the new drugs. however, there come two main challenges in using textural information for ddi prediction. first, the descriptions are professional with many special tokens and expressions. therefore, the general large language models (llms) like chatgpt and gpt4 foot_2 are inadequate in dealing with this scenario. additionally, the texts are lengthy and contain noise, leading to elevated computational expenses and heightened training complexities for lms. to address these challenges, we design a specific prompt for the ddi prediction task and finetune a 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. our approach, named textddi, consists of two modules: an lm-based ddi predictor and an rlbased information selector. with the specially designed task prompt, the lm-based ddi predictor is first trained to predict interactions based on randomly sampled drug descriptions, effectively capturing domain-specific knowledge. to appropriately describe the drugs, we design an rl-based information selector to generate drug descriptions for each drug pair as its prompt. specially, the generated drug descriptions are conditioned on drug pairs to improve flexibility. by learning from the reward returned from the ddi predictor, the information selector effectively selects suitable sentences that align with the given pairs of drugs. the main contributions can be summarized as follows: ## 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 description 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 appendix b, but we limit the length of the generated prompt for each drug pair to only a few hundred tokens, e.g., 256 tokens. our method, named textddi, consists of two modules: an lm-based ddi predictor and an rl-based information selector. the ddi predictor predicts the type of drugdrug interaction based on the specially designed prompt of the drug pair, while the rl-based information selector is responsible for generating the corresponding prompt based on the drug descriptions of the drug pair. additionally, we alternate the training pipeline of these two modules to facilitate their mutual enhancement. figure 2 illustrates an overview of our method in the inference stage. ## conclusion we propose a problem setup that predicts ddi for new drugs as a zero-shot setting. to achieve this, we propose textddi, a novel framework that capitalizes on textual descriptions of each single drug. textddi consists of an lm-based ddi predictor and an rl-based information selector. the lm-based ddi predictor is trained with drug-pairdependent descriptions to capture domain-specific knowledge. the rl-based information selector is designed to generate shorter but more relevant descriptions for each drug pair and is trained with ddi predictor serving as 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 descriptions generated by the information selector are semantically relevant to the target prediction.