ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design

Authors: Keir Adams, Kento Abeywardane, Jenna Fromer, Connor W. Coley
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: 3D molecular generation, drug design, molecules

Abstract:
Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the joint distribution over 3D molecular structures and their interaction profiles may facilitate 3D interaction-aware chemical design. We specifically design ShEPhERD, an SE(3)-equivariant diffusion model which jointly diffuses/denoises 3D molecular graphs and representations of their shapes, electrostatic potential surfaces, and (directional) pharmacophores to/from Gaussian noise. Inspired by traditional ligand discovery, we compose 3D similarity scoring functions to assess ShEPhERD’s ability to conditionally generate novel molecules with desired interaction profiles. We demonstrate ShEPhERD’s potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging.

Decision: accept (oral)
Reviewer scores: 8, 10, 6

Reviews and discussion:
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creation date:
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thread_type:
official_review

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creator: reviewer_jqjd

<content>
summary:
The paper introduces the ShEPhERD method for generating 3D molecules that satisfy specific shape, electrostatic surface, and pharmacophore constraints. The method represents molecular shapes, electrostatic surfaces, and pharmacophores as point clouds and jointly models these properties with the 3D molecular structure. During sampling, inpainting techniques are used to generate molecules that meet the specified properties. This method enriches ligand-based drug design approaches and shows significant innovation in its application areas. However, it lacks substantial innovation in the basic structure of the generative algorithm.

strengths:
1. The method provides a comprehensive summary of related work. To my knowledge, it is currently the only 3D generative model that simultaneously incorporates shape and pharmacophore constraints.
2. The authors achieved credible results in three specific tasks, partially demonstrating the method’s effectiveness.

weaknesses:
1. The method represents shapes and electrostatic surfaces as point clouds and generates them together with the molecular structure using 3D graph neural networks, which may significantly impact computational efficiency. The generative model based on DDPM may also have efficiency drawbacks. The authors should provide relevant statistical results.
2. The method lacks comparisons with other methods, casting doubt on the superiority of the results.
    (1). For shape-based molecular generation, methods like ShapeMol and SQUID can achieve similar effects and should be compared. (2) For pharmacophore-based molecular generation, both pharmacophore type matching and structural matching should be considered and evaluated separately. If combined comparison is necessary, reference to 2D pharmacophore works such as the Match Score (Nature Communications | (2023)14:6234) is suggested. (3) The authors should provide the similarity of generated molecules to reference molecules for each specific task to ensure that the generated molecules are not simple variations of the reference molecules.

questions:
My questions are the same as mentioned in Weaknesses.
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creation date:
1730699674305

thread_type:
official_review

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creator: reviewer_pbc2

<content>
summary:
This paper presents a new method for the generation of small molecules conditioned on shape, electrostatic potential surface, or pharmacophores.  This development provides a useful and practical tool that can complement multiple conventional tasks during various stages in drug design projects.  The method is a joint diffusion model that predicts molecules in terms of graphs plus coordinates, surfaces sampled on a modest number of points (with or without an electrostatic potential), and pharmacophores (types and coordinates).  The conditional generation uses inpainting to sample the molecule graphs and coordinates conditioned on one or more other properties of the molecule, which can mimic a known target molecule.

strengths:
The paper presents a substantial advance compared to previous related methods; it has a strong logical foundation with clear exposition of the method and its parameters, and a diverse set of demonstrations.  The conditional generation of molecules based on ligand-only properties is important in applied drug discovery and the method offers both a practical way for thinking about some of the concepts and a tool that can be readily applied.  In addition, it introduces useful and practical datasets, and it defines a simple but clear metric for comparing surface shapes.  I think that the work opens up a logical framework for building practical tools in drug discovery and will influence followup research.

weaknesses:
Most of the weaknesses of the paper are somewhat superficial and do not limit the importance of this contribution.

All the conditional generation requires providing n1 a priori for each sample, and the pharmacophore application, which is particularly interesting and useful, requires providing both n1 and n4.  In addition to sampling n4 based on the chosen n1 and the training data distribution, have the authors considered exploring ways to automate or predict values for parameters like n1 and n4?  It would be very interesting to explore the regime where compute limitations are of no concern.

The demonstration of conditional generation in Fig 3 is performed on a random subsample of the training molecules.  I understand that there is no perfect split of molecules, however, the jump from a random subset, which work rather well, to natural products, which are completely out of distribution and don't appear to work well (albeit better than reinvent), appears a bit too dramatic.  To enhance the robustness of the evaluation and to gain additional insights, the authors could create additional, harder, splits by molecular properties such as numbers of atoms or rings, splits by scaffold, or splits by similarity clustering of the training data. Because the authors provide two new training datasets, it is perhaps more important than it would otherwise have been to provide and characterize a number of distinct, increasing difficulty splits.

The surfaces were fit on modest numbers of points and the graph embedding networks are surprisingly small, which are all probably a result of trying to keep the graph neural nets from collapsing.  It is possible that these shortcuts (and the usual problems of graph neural nets and scaling to larger systems) lead to the size-dependent effects demonstrated in the supplemental materials; these limitations might also underpin the examples of natural products.  It may be worth adding a brief note to discuss the scaling of the method with the size of the small molecules, even if the detailed analysis is reserved for the supplemental materials due to space limitations.

questions:
Although it is not unreasonable, what is the logic for fitting the surfaces onto the volumes in order to obtain the parameters required for the method?  Also, why only fitting the particular set of discrete numbers of points (50, 100, 150, 200, 300, 400) and not, say, a function of the number of atoms, or of the total surface size?  Would there be a problem with scaling the method to a large number of points, or is it simply deemed unnecessary?  As a minor related question, what is the purpose of a separate set of surface points for x2 compared to x3?  Wouldn't the coordinates of x3 suffice for both tasks?

Other than following prior work, do the authors have a high level rationale for selecting the particular subset of targets from the PDB in Zheng et al (2024)?
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creation date:
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thread_type:
official_review

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creator: reviewer_hw8x

<content>
summary:
This paper introduces ShEPhERD, a generative SE(3)-equivariant diffusion model for molecular drug design that incorporates 3D structural features inspired by medicinal chemistry, namely shape, electrostatics, and pharmacophoric properties. The authors demonstrate ShEPhERD's utility for ligand-based drug design tasks, including ligand hopping, bioactive hit diversification, and bioisosteric fragment merging.

I appreciate the approach of leveraging prior target and chemical information to enhance generative models and commend the authors for advancing in this direction. While the case studies on specific targets are excellent, the general evaluation and particularly the comparative analysis with other methods could be significantly strengthened. I am inclined to give a strong score if these concerns are addressed.

strengths:
- The paper is comprehensive and well-written, with excellent figures and molecular visualizations.
- Although conditioning on pharmacophoric features is not new in the field, this is the most thoughtful implementation of the approach to date and a very important direction more works should look into. The authors obviously have a very good understanding of drug discovery and SBDD that is not open seen at ML conferences.
- In my opinion, the field is overly obsessed with only focusing on de novo design models that cannot leverage any target specific data. Therefore, I really like the fact they were able to use experimental fragments screens to get better designed molecules.
- The inclusion of comparisons to REINVENT and random docking screens as baselines is a valuable addition, as these are seldom seen in DDPM literature. (However, I believe there is still room for improvement in the comparisons and evaluations—see below).

weaknesses:
**Main limitations:**

- While the method is strong, main limitation of the paper as it currently stands is the extreme lack of comparisons to related works in the experiments conducted. I kindly request the authors to benchmark against previous works in the relevant tasks. I am not looking for SOTA on all metrics, but better benchmarking is essential to understand the strengths and limitations of the work and I cannot recommend a strong accept at ICLR without more comparisons.
    - There are multiple works in a similar flavour that propose inpainting and/or conditioning on pharmacophore features of some kind. E.g.  DiffSBDD+inpainting [1], MolSnapper [2] and Diffint [3].
    - Compare to DiffHopp [4]/TurboHopp [5] in case of hit diversification. Obviously these models are only in the structure-based context.
    - Compare to ChemProjector [6]/SynFormer [7] incase of ligand hopping.
- Furthermore, the metrics could be greatly improved:
    - Heavy reliance on only Vina scores and shape/tanimoto similarity. 
    - The work cites the PoseCheck [8] paper but does not use any of the metrics. For example, clashes are not measured. In the target conditioned cases, does ShEPhERD provide poses without clashes before relaxation? Is the model able to design hydrogen bonding networks better than previous SBDD models?
    - Strain energy is calculated in A.3 and the model appeared to perform well. However, the impact of this is greatly diminished by not comparing to other works. I would be grateful if the authors could include this to give context. Do the authors use the implentation in PoseCheck or their own? Is the strain energy calculated for poses bound inside the protein pocket?
    - PoseBusters [9] validity could also be used to access the quality of the structures.
- An expanded related work section covering models that perform ligand hopping, scaffold hopping and fragment merging would base the work in the literature much better.
- Would be nice to see anonymous code in the review stage.
- In the hit diversiation section, the authors claim that ShephERD produces enriched sampled “Despite having no knowledge of the protein targets”. While it is technically true the model has never directly see the receptor coordinates, a large amount of structural information is leaked/provided based on the complimentarity of the 7 experimental ligands. This is not a weakness of the method but I believe the language to be misleading. A simple rewording should do.
- The joint model/inpainting method used to condition on the target profile is not novel, as this was done in DiffSBDD 2 years ago [1]. A citation to that work and the original RePaint paper reproducing the ‘replacement’ method of inpainting in the main text would suffice.
- In the interaction-conditioned generation section you only generate 20 samples per target and filter based on interaction similarity. How many samples are left after filtering? How can you ensure statistical significance?
- It is not clear to me if any attempt was made to reduce data leakage between train and test sets (except for size) as the splits seem random. How can you be sure that the target interaction profile on the test samples are not the same/similar to those seem during training?
- Comparison to REINVENT.
    - Was REINVENT trained to convergence? 10,000 oracle calls seems quite low in my experience? (Happy to be proved wrong)
    - Can the authors clarify exactly which version of REINVENT they use? They seem to use an old version from the PMO benchmark. This is the 2017 implementation I believe? I would encourage them to use the largest REINVENT4, this is easy to use and a strong baseline in my experience (https://github.com/MolecularAI/REINVENT4).
    - Little measure of synthetic accessibility in the work. REINVENT does surprisingly well on SA score in my experience. Can this be benchmarks quantitively?

**Minor points:**

- Some background on various inpainting/conditioning strategies for DDPMs would be nice (i.e. replacement method [what is used here], classifier guidance, reconstruction guidance, Doobs-H transform)
- The generated samples in Appendix A.10 are quite small. It would be nice to include some larger samples as well to see how the model handles larger structures.
- The neural network architecture is described in great detail. In my opinion taking too much space. I would prefer to see this in the appendix with more discussion on experiments in the main text.
- What was the intuition for scaling the pharmacophore feature vector by 2.0?
- Can the target profile guidance be more or less ‘weakly’ enforced?
- It would be nice to see the fragment merging work replicated on more than one target. I would recommend the Mpro screen (on Fragalysis already) for example. You can then compare for novelty against the molecules derived from than screen in the COVID moonshot- To what extent does conditioning with the ShEPhERD method limit diversity of new samples? Could be that previous DDPMs produce on average worse samples but allow for the exploration of novel chemotypes? (My guess is this method is still very useful but I would like to see this discussed/measured)
- Have you tried an ablation of the joint noising regime? I believe in DiffSBDD its benefits was found to be marginal. [1]
- Could your pharmacophore similarity metric be seen as related to interaction fingerprint similarity? 
- Is it possible to use ShEPhERD completely de novo? I.e. for a new target without example molecules in the PDB? Could pharmacophore features be constructed from hotspot maps/manual feature engineering?
- The fragment merging protocol is nice, can a semi-automated pipeline be provided in the code?
- Section A.9 correctly points out the disparity between the size of ligand seen during training and those seen during evaluation. Why not include larger ligands in the training set?
- Given the large training set size, it would be nice to see some scaling law experiments in terms of dataset and model size. (Given that I am not aware of the resources available to the authors this will not impact my review score - I am just interested if its possible)..
    - Can you provide a list of the 7 PDBs used to perform these experiments?
- Figure 1 + 2:
    - Are both very good: I would consider merging to safe space and add more analysis/discussion.
- Figure 3:
    - Can ‘dataset samples’ be added to the histograms in the right of the figure?
- Figure 4:
    - The PyMol in this paper is generally very good but the overlapping molecules in this figure is highly confusing. A key/color map would be very helpful. (Especially for hit diversification section).
    - REINVENT molecules seem to consistently outperform ShEPhERD on SA scores. I would be good to include an example structure instead of just listing the metrics.
    - The PDB targets are quite well chosen (Kinases/COVID MPro etc), adding target names in brackets next to PDBs would be helpful.


1. Schneuing, Arne, et al. "Structure-based drug design with equivariant diffusion models." arXiv preprint arXiv:2210.13695 (2022).
2. Ziv, Yael, Brian Marsden, and Charlotte M. Deane. "Molsnapper: Conditioning diffusion for structure based drug design." bioRxiv (2024): 2024-03.
3. Sako, Masami, Nobuaki Yasuo, and Masakazu Sekijima. "DiffInt: A Pharmacophore-Aware Diffusion Model for Structure-Based Drug Design with Explicit Hydrogen Bond Interaction Guidance." (2024).
4. Torge, Jos, et al. "Diffhopp: A graph diffusion model for novel drug design via scaffold hopping." arXiv preprint arXiv:2308.07416 (2023).
5. Yoo, Kiwoong, et al. "TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models." arXiv preprint arXiv:2410.20660 (2024).
6. Luo, Shitong, et al. "Projecting Molecules into Synthesizable Chemical Spaces." arXiv preprint arXiv:2406.04628 (2024).
7. Gao, Wenhao, Shitong Luo, and Connor W. Coley. "Generative Artificial Intelligence for Navigating Synthesizable Chemical Space." arXiv preprint arXiv:2410.03494 (2024).
8. Harris, Charles, et al. "Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses." NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. 2023.
9. Buttenschoen, Martin, Garrett M. Morris, and Charlotte M. Deane. "PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences." Chemical Science 15.9 (2024): 3130-3139.

questions:
- To what extent does conditioning with the ShEPhERD method limit diversity of new samples? Could be that previous DDPMs produce on average worse samples but allow for the exploration of more novel chemotypes? (My guess is this method is still very useful but I would like to see this discussed/measured)
- Have you tried an ablation of the joint noising regime? I believe in DiffSBDD its benefits was found to be marginal.
- Could your pharmacophore similarity metric be seen as related to interaction fingerprint similarity? 
- Is it possible to use ShEPhERD completely de novo? I.e. for a new target without example molecules in the PDB? Could pharmacophore features be constructed from hotspot maps/manual feature engineering?
- The fragment merging protocol is nice, can a semi-automated pipeline be provided in the code?
- Section A.9 correctly points out the disparity between the size of ligand seen during training and those seen during evaluation. Why not include larger ligands in the training set?
- Given the large training set size, it would be nice to see some scaling law experiments in terms of dataset and model size. (Given that I am not aware of the resources available to the authors this will not impact my review score - I am just interested if its possible).
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creator: authors

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title:
Common Response to all reviewers highlighting new experiments and comparisons to related work (Part 1/n)

comment:
We appreciate the reviewers’ careful consideration of our submission and for their many detailed comments and suggestions to improve our work. We thank the reviewers for their patience as we’ve performed a number of new experiments and comparisons in order to highlight the flexibility and performance of ShEPhERD across ligand-based and structure-based drug design tasks. While we will respond to each reviewer separately, this comment provides an overview of the new experiments and evaluations, which we believe should be highlighted for all the reviewers. These new comparisons include:

- Comparisons to SQUID for shape-conditioned molecular generation on MOSES.
- Comparisons against structure-based drug design models that explicitly encode the protein pocket.
- Comparisons specifically against DiffSBDD.
- Comparisons against SynFormer on natural product ligand hopping.

These comparisons are reported in the new Appendices B.1, B.2, B.3, and B.4, which are included at the end of the submission file so that they can be easily found during this review period. These new sections will be better integrated into the relevant Appendices for the final version of the paper.

**Comparisons to SQUID for shape-conditioned molecular generation on MOSES**

Reviewer JQjD suggested that we compare to SQUID (Adams and Coley, ICLR 2023) to evaluate the superiority of ShEPhERD for shape-conditioned molecular design. To perform this comparison, we apply the version of ShEPhERD trained on MOSES-aq to learn P(x1,x3,x4) to generate new molecules conditioned on the surface shape (e.g., the coordinates of x3, which implicitly define x2) of the 1000 3D molecules in SQUID’s test set. Note that SQUID and ShEPhERD use the same training and test splits of MOSES (which are directly provided by the original MOSES paper), and hence there was no data leakage. We condition the P(x1,x3,x4) model on the molecules’ shapes only by inpainting just the coordinates of x3, and allowing ShEPhERD to generate x1, x4, and the electrostatic potentials of x3. We generate up to 10 samples from ShEPhERD and compare the average volumetric shape similarity using ROCS (the same shape scoring function that SQUID used). Since SQUID’s generated molecules for each test molecule are publicly available for download on their github/figshare, we re-evaluated the average shape similarity of SQUID-generated molecules to ensure a fair comparison.

Across the 1000 test-set molecules, the average shape similarity of SQUID-generated molecules is 0.70 using lambda=1.0, and 0.74 using lambda=0.3. In contrast, the average shape similarity of ShEPhERD-generated molecules is 0.80, a substantial improvement. Moreover, the ShEPhERD-generated molecules have an average 2D graph similarity of just 0.22 compared to the reference molecule, whereas the SQUID-generated molecules have average graph similarities of 0.25 (lambda=1.0) and 0.35 (lambda=0.3). Hence, ShEPhERD is able to generate less chemically similar molecules that have higher shape similarity to the target, on average.

We emphasize that ShEPhERD greatly exceeds the performance of SQUID even with this indirect way of doing shape-conditioned generation, i.e. via inpainting the joint P(x1,x3,x4) model. We could also train a P(x1,x2) model directly for this task; however, we did not have time to retrain ShEPhERD from scratch within the rebuttal time period. We also note that ShEPhERD conditions on surface shapes, whereas we have compared ShEPhERD to SQUID using volumetric shape similarity (with ROCS). We expect that we could further improve ShEPhERD’s performance in shape-conditioned generation by retraining ShEPhERD with a smaller surface probe radius, thereby “shrinking” the surface shape to better model volumetric shapes.
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Metareview:
metareview:
In this work, authors introduce ShEPhERD, an SE(3)-equivariant diffusion model for 3D molecular generation that jointly models molecular graphs, shapes, electrostatic surfaces, and pharmacophores. The model enables conditional generation of molecules with desired 3D interaction properties through inpainting, facilitating tasks like natural product ligand hopping and bioisosteric fragment merging.

The main scientific contribution is demonstrating that a joint diffusion model over multiple molecular representations enables generating novel molecules that preserve key 3D interaction patterns of reference compounds. The authors validate this through experiments showing ShEPhERD can: 1) generate molecules with similar shapes to references while maintaining low graph similarity, outperforming SQUID as shown in additional experiments, 2) generate bioisosteric analogs of natural products that maintain interaction patterns while having lower synthetic accessibility scores, and 3) merge fragments based on their pharmacophoric interactions.

The reviewers identified several key strengths. Reviewer HW8X highlighted the thoughtful implementation of pharmacophore conditioning and practical utility for drug discovery applications. Reviewer pbc2 noted the strong logical foundation and clear exposition. The extensive comparisons to baselines like REINVENT and response to reviewer requests for additional comparisons to methods like SQUID, DiffSBDD, and SynFormer demonstrate robust performance.

Some limitations were raised, particularly around computational efficiency and scaling to larger molecules, as noted by Reviewer JQjD. However, the authors acknowledge these engineering challenges as future work, appropriately focusing first on establishing the method's utility. Questions about dataset splits and evaluation protocols were addressed through additional experiments showing good performance on challenging out-of-distribution tasks.

I recommend acceptance for several reasons: First, the paper makes a clear technical contribution by introducing a novel joint modeling approach for 3D molecular properties. Second, the extensive experimental validation, including new comparisons added during discussion, demonstrates practical utility across multiple drug design tasks. Third, the method enables important capabilities not possible with existing approaches, particularly around preserving 3D interactions in ligand-based design. While there are opportunities for improving efficiency and scaling, these do not diminish the core methodological advance. The thorough author response and additional experiments effectively addressed the key reviewer concerns about comparisons and evaluation protocols.

In summary, this work represents an important contribution to molecular generation methods that will benefit both the machine learning and drug discovery communities. The clear exposition, comprehensive evaluation, and demonstrated practical utility make it worthy of presentation at ICLR.