Paper: Drug Discovery with Dynamic Goal-aware Fragments (ICLR-2024)

Reviewer comment (Novelty):
The main concern is about the novelty. The three techniques, i.e., information bottleneck, soft-actor critic (SAC), and genetic algorithm (GA), are well known methods. Seems the authors just make a pipeline to combine all existing methods together.

Author response:
We want to emphasize that the contribution of our work lies in the **identification of the specific limitations in existing fragment-based drug discovery (FBDD) models and combination of the fragment extraction, fragment assembly and fragment modification modules in a novel way to overcome the limitations.** Specifically, we have pointed out that existing FBDD models have the following limitations: they do not take the target chemical properties into account when extracting fragments, rely on heuristic fragment selection rules, and/or the exploration is confined to the initial fragment vocabulary.

To effectively overcome these, we have proposed the combination of the three modules that were carefully chosen for how they combine: the fragment extraction module provides goal-aware fragments to the fragment assembly module, the fragment assembly module provides high-quality population to the fragment modification module, and the fragment modification module provides novel fragments to the fragment extraction module. Through the extensive ablation studies, we have shown that the outstanding performance of GEAM **cannot be achieved by each individual component of GEAM alone**, and that **only the proposed combination of the three modules can effectively explore the chemical space to discover novel and diverse drug candidates.**

Moreover, even at the component level, we have newly proposed **FGIB as the fragment extraction module, which considers fragments as its substructure recognition unit**. This feature makes FGIB more suitable for constructing fragment vocabularies for subsequent generation, in contrast to previous GIB-based substructure recognition methods that commonly recognize disconnected substructures in a single molecule. Additionally, we have  proposed to construct the initial population of GA with molecules generated by SAC, which significantly improves exploration in chemical space compared to Graph GA [1] that draws its initial population from the training set.

We believe that combining existing methods to establish a new framework and showing its emergent effects is a significant research contribution, rather than a trivial one. While Stable Diffusion [2] can be thought of as nothing more than combining the already well-known VQ-VAE [3,4] and diffusion [5], its demonstrated practical impact highlights the significance of its research contribution. Since we proposed to integrate the three modules into a single framework and experimentally validated its effectiveness, we believe that our work is making an important contribution that can have a practical impact and spawn many interesting future studies.

Reviewer comment (Theory):
What are the specific challenges to use information bottleneck, SAC, and GA, in molecule generation tasks? How are they mitigated?

Author response:
We want to emphasize that the contribution of our work lies in the **identification of the specific limitations in existing fragment-based drug discovery (FBDD) models and combination of the fragment extraction, fragment assembly and fragment modification modules in a novel way to overcome the limitations.** Specifically, we have pointed out that existing FBDD models have the following limitations: they do not take the target chemical properties into account when extracting fragments, rely on heuristic fragment selection rules, and/or the exploration is confined to the initial fragment vocabulary.

To effectively overcome these, we have proposed the combination of the three modules that were carefully chosen for how they combine: the fragment extraction module provides goal-aware fragments to the fragment assembly module, the fragment assembly module provides high-quality population to the fragment modification module, and the fragment modification module provides novel fragments to the fragment extraction module. Through the extensive ablation studies, we have shown that the outstanding performance of GEAM **cannot be achieved by each individual component of GEAM alone**, and that **only the proposed combination of the three modules can effectively explore the chemical space to discover novel and diverse drug candidates.**

Moreover, even at the component level, we have newly proposed **FGIB as the fragment extraction module, which considers fragments as its substructure recognition unit**. This feature makes FGIB more suitable for constructing fragment vocabularies for subsequent generation, in contrast to previous GIB-based substructure recognition methods that commonly recognize disconnected substructures in a single molecule. Additionally, we have  proposed to construct the initial population of GA with molecules generated by SAC, which significantly improves exploration in chemical space compared to Graph GA [1] that draws its initial population from the training set.

We believe that combining existing methods to establish a new framework and showing its emergent effects is a significant research contribution, rather than a trivial one. While Stable Diffusion [2] can be thought of as nothing more than combining the already well-known VQ-VAE [3,4] and diffusion [5], its demonstrated practical impact highlights the significance of its research contribution. Since we proposed to integrate the three modules into a single framework and experimentally validated its effectiveness, we believe that our work is making an important contribution that can have a practical impact and spawn many interesting future studies.

Reviewer comment (Reproducibility):
What are the specific challenges to use these three methods together?

Author response:
We want to emphasize that the contribution of our work lies in the **identification of the specific limitations in existing fragment-based drug discovery (FBDD) models and combination of the fragment extraction, fragment assembly and fragment modification modules in a novel way to overcome the limitations.** Specifically, we have pointed out that existing FBDD models have the following limitations: they do not take the target chemical properties into account when extracting fragments, rely on heuristic fragment selection rules, and/or the exploration is confined to the initial fragment vocabulary.

To effectively overcome these, we have proposed the combination of the three modules that were carefully chosen for how they combine: the fragment extraction module provides goal-aware fragments to the fragment assembly module, the fragment assembly module provides high-quality population to the fragment modification module, and the fragment modification module provides novel fragments to the fragment extraction module. Through the extensive ablation studies, we have shown that the outstanding performance of GEAM **cannot be achieved by each individual component of GEAM alone**, and that **only the proposed combination of the three modules can effectively explore the chemical space to discover novel and diverse drug candidates.**

Moreover, even at the component level, we have newly proposed **FGIB as the fragment extraction module, which considers fragments as its substructure recognition unit**. This feature makes FGIB more suitable for constructing fragment vocabularies for subsequent generation, in contrast to previous GIB-based substructure recognition methods that commonly recognize disconnected substructures in a single molecule. Additionally, we have  proposed to construct the initial population of GA with molecules generated by SAC, which significantly improves exploration in chemical space compared to Graph GA [1] that draws its initial population from the training set.

We believe that combining existing methods to establish a new framework and showing its emergent effects is a significant research contribution, rather than a trivial one. While Stable Diffusion [2] can be thought of as nothing more than combining the already well-known VQ-VAE [3,4] and diffusion [5], its demonstrated practical impact highlights the significance of its research contribution. Since we proposed to integrate the three modules into a single framework and experimentally validated its effectiveness, we believe that our work is making an important contribution that can have a practical impact and spawn many interesting future studies.

Reviewer comment (Novelty):
The paper could enhance its clarity by describing the specific advantages of FGIB over existing substructure identification architectures based on GIB theory.

Author response:
The advantage of the proposed FGIB is that **its substructure recognition unit is fragments rather than atoms, which makes FGIB more suitable for constructing fragment vocabularies for subsequent generation.** As we explained in the paper, subgraph recognition by GIB has been only employed in classification and regression tasks, and this is the first work that applies GIB to fragment extraction. In practice, previous subgraph recognition methods are inadequate for extracting connected fragments, as they commonly recognize disconnected or atomic substructures in a single molecule. We have additionally included this explanation in Related Work.

Reviewer comment (Theory):
Lack of clear intuition for the loss proposed in Equation 5, focusing on how the optimization of the first term contributes to the identification of the important fragments.

Author response:
**Optimization of both the first and second terms in Equation 5 contributes to the identification of the important fragment.** The first term and the second term are the upper bounds of $-I(Z,Y;\theta)$ and $\beta I(Z,G;\theta)$, respectively. Intuitively, minimizing $-I(Z,Y;\theta)$ encourages representation of chosen fragments to be expressive for predicting a label $Y$ while minimizing $\beta I(Z,G;\theta)$ encourages choosing sparse fragments to forget a graph $G$. Therefore, optimizing only the first terms without considering the second term, which enforces the selection of sparse fragments, leads to a trivial solution where the model chooses all the fragments of the graph to predict the label.

Reviewer comment (Reproducibility):
The set of properties Y for each molecule in the training set is generated using Equation 11. Could you please provide more information on how the terms QED and SA are computed for each molecule?

Author response:
Following many existing works and all the baselines of our paper, QED and SA were computed using the RDKit library [1]. We have additionally included this detail in Section C.2.

Reviewer comment (Presentation):
Some papers in the bibliography are cited with the arXiv version while there exists a peer-reviewed version. Is this because the arXiv version contains additional information not present in the peer-reviewed version, or is there another reason?

Author response:
We have corrected the citation of the Yang et al. paper [2]. Thank you for pointing this out.

Reviewer comment (Evaluation):
In the experimental part, the method proposed in the article did not achieve the best results on some data or some indicators.

Author response:
In our paper, **we evaluated the effectiveness of our proposed GEAM on a total of twelve tasks and showed that GEAM performed best on eleven tasks**. In the five binding affinity optimization tasks, GEAM outperformed all the baselines by a very large margin (Table 1 and Table 2). In the PMO MPO tasks, GEAM achieved the best results in six out of the seven tasks (Table 5). In addition, the novelty and diversity values of GEAM are at the best level compared to the baselines except MORLD and REINVENT  (Table 3, Table 4 and Table 6). As we explained in the paper, the high novelty values of MORLD are trivial due to its poor optimization performance and very low diversity, and the high diversity values of RationaleRL on the target proteins 5ht1b and jak2 are not meaningful due to its poor optimization performance and novelty.

We emphasize that as we explained in the paper, there is a general trend that the more powerful the molecular optimization model, the less likely it is to generate diverse molecules [1], but our proposed GEAM overcomes this trade-off by discovering novel and high-quality goal-aware fragments on-the-fly. Among the models tested in our paper, **GEAM is the only one that consistently showed excellent performance in all three aspects of optimization performance, novelty and diversity, suggesting that GEAM is highly suitable for drug discovery.**

Reviewer comment (Experiments):
The method will add the fragments generated by the model to the vocabulary.

Author response:
We have qualitatively and quantitatively examined the effectiveness of generated fragments. Through the qualitative analysis in Figure 3(d), **we have shown that the fragments extracted by the FGIB module match well with chemical knowledge**. We also have performed extensive ablation studies to verify the quality of the extracted fragments by the FGIB module (Figure 3(a) and Figure 3(b)). In particular, when comparing GEAM and GEAM-static, we can observe that adding fragments extracted during generation does not degrade performance, but improves novelty and diversity. Overall, we can conclude that **the FGIB module and the extracted fragments by the module are reliable and help generate novel and diverse drug candidates.**

Reviewer comment (Evaluation):
Will this introduce illusion or false information?

Author response:
We have qualitatively and quantitatively examined the effectiveness of generated fragments. Through the qualitative analysis in Figure 3(d), **we have shown that the fragments extracted by the FGIB module match well with chemical knowledge**. We also have performed extensive ablation studies to verify the quality of the extracted fragments by the FGIB module (Figure 3(a) and Figure 3(b)). In particular, when comparing GEAM and GEAM-static, we can observe that adding fragments extracted during generation does not degrade performance, but improves novelty and diversity. Overall, we can conclude that **the FGIB module and the extracted fragments by the module are reliable and help generate novel and diverse drug candidates.**

Reviewer comment (Evaluation):
Should there be a section to verify the reliability and authenticity of the generated snippets?

Author response:
We have qualitatively and quantitatively examined the effectiveness of generated fragments. Through the qualitative analysis in Figure 3(d), **we have shown that the fragments extracted by the FGIB module match well with chemical knowledge**. We also have performed extensive ablation studies to verify the quality of the extracted fragments by the FGIB module (Figure 3(a) and Figure 3(b)). In particular, when comparing GEAM and GEAM-static, we can observe that adding fragments extracted during generation does not degrade performance, but improves novelty and diversity. Overall, we can conclude that **the FGIB module and the extracted fragments by the module are reliable and help generate novel and diverse drug candidates.**

Reviewer comment (Experiments):
The lack of verification of snippet reliability may be the reason why experimental results do not show consistent superiority.

Author response:
We have qualitatively and quantitatively examined the effectiveness of generated fragments. Through the qualitative analysis in Figure 3(d), **we have shown that the fragments extracted by the FGIB module match well with chemical knowledge**. We also have performed extensive ablation studies to verify the quality of the extracted fragments by the FGIB module (Figure 3(a) and Figure 3(b)). In particular, when comparing GEAM and GEAM-static, we can observe that adding fragments extracted during generation does not degrade performance, but improves novelty and diversity. Overall, we can conclude that **the FGIB module and the extracted fragments by the module are reliable and help generate novel and diverse drug candidates.**