Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step. We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm. We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization. This simple, powerful technique operates strictly on the inputs and outputs of any translation model. We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models. Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "for" loop. Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.
This paper presents a translation-based method for molecular property optimization. It uses a sequence- or graph-based encoder/decoder framework to produce molecules with (hopefully) improved properties, then feeds a subset of these molecules back into the encoder/decoder to generate a new set of molecules; this process is repeated for a fixed number of iterations to arrive at a final set of "optimized" molecules. The method is agnostic to the form of the encoder/decoder; the emphasis is on the iterative approach. Additionally, this approach enables visualization of "molecular" traces that can reveal pathways between molecules that follow relationships similar to matched molecular pairs. The work extends related work in translation-based property optimization [6]. The paper is well-written and generally easy to read.
The method is evaluated on two tasks, logP and QED. Both of these are computed properties that have known issues (see the discussion in [3]), but I understand that these properties are used in many publications and are thus easy to compare. The method presented here performs similar to others on a QED task. They claim superior results on the logP task, but I have concerns about the fairness of the comparison since logP can be exploited by very simple models if there are no limits on the size of the generated molecules (or, similarly, the number of tokens/generative steps allowed for each molecule). Additionally, the authors claim to perform multi-objective optimization but do not actually do this.
The iterative nature of this method is very interesting. However, my concerns about the types of experiments and comparisons that were done (see below for more details) are big enough that I cannot approve this paper in its current form. Weak reject.
Specific notes (starting with page number):
- 1: "potential druggable candidates" does not make sense; compounds are not "druggable" (their targets are), although they may be "drug-like".
- 2: Consider citing Kramer et al.'s seminal work on matched molecular pairs [1].
- 3: Please explain what it means for y to "paraphrase" x?
- 5: For your logP experiments, you need to be more clear about how you are comparing to other models. You are guaranteed to get to higher logP values if you can generate larger molecules (more tokens) than the baselines, since logP is essentially linear in the number of carbons. Are you doing something to limit the number of tokens you can generate in each iteration? Or why should I believe these comparisons are fair?
- 5: In Table 1, note that some literature uses a "normalized" penalized logP, while others use the formula directly without a dataset-specific normalization (which can appear to give better results). Can you confirm which you are using here and whether the baseline models are the same?
- 5: The results in Table 1 would be more compelling if they were not divorced from their starting points. Please include information about the similarity of these molecules to the starting molecule as well as the property delta. Consider an approach like Jin et. al [2], where results were specifically categorized by similarity constraints.
- 5: "All models were trained on the open-source ZINC dataset." What subset of ZINC are you using?
- 5: The supplementary figure showing that logP is broken is missing?
- 5: "Consistent with the literature we report diversity as...". Please cite some literature that you are consistent with.
- 6: "we sample 100 times from a top-2..."; does this mean you are doing 100 iterations? Sampling 100 times from the same top-2 sampler doesn't really make sense, but I'm not entirely sure what you are describing here.
- 7: Figure 4 says these are "ablation" experiments. What exactly are you ablating?
- 8: You state that better performance on logP and similar performance on QED is not known in the literature. In fact, the MolDQN paper [3] calls this out explicitly (and also contains a discussion of bounded vs. unbounded logP).
- 8: "Recent RL methods focus on molecular construction and are therefore not well-suited for the generation of molecular traces"; I disagree with this. RL methods that can start from a predefined graph have the ability to move between compounds, possibly in a way that is orthogonal to traditional similarity-based exploration (see the discussion of "MDP edit distance" in [4]). Also note that one of the key features of graph-based generators like [2] and [5] is that all of the intermediate states are valid, so you could do similar molecular traces for interpretability (although your differences are more like MMPs with functional group-level deltas).
- 9: "synthetic chemists can carry out the individual steps of a molecular trace..."; in general this is not true. The known medicinal chemistry transformations are a relatively small set of operations, and your molecular traces are unlikely to capture them in any systematic way. Please avoid making claims like this unless you can back them up with experimental evidence or comparisons to models explicitly trained for synthetic route planning.
- 9: These experiments are not multi-property optimization. Measuring the value of a secondary property while optimizing a primary property is not the same as optimizing them both simultaneously. The latter requires some strategy for incorporating both property values into the decision function, such as scalarizing (see "multi-objective optimization" on Wikipedia).
References:
[1] Kramer, C. et al. Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA). J. Med. Chem. 61, 3277–3292 (2018).
[2] Jin, W., Barzilay, R. & Jaakkola, T. Junction Tree Variational Autoencoder for Molecular Graph Generation. arXiv [cs.LG] (2018).
[3] Zhou, Z., Kearnes, S., Li, L., Zare, R. N. & Riley, P. Optimization of Molecules via Deep Reinforcement Learning. Sci. Rep. 9, 10752 (2019).
[4] Kearnes, S., Li, L. & Riley, P. Decoding Molecular Graph Embeddings with Reinforcement Learning. arXiv [cs.LG] (2019).
[5] You, J., Liu, B., Ying, R., Pande, V. & Leskovec, J. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. arXiv [cs.LG] (2018).
[6] Jin, W., Yang, K., Barzilay, R. & Jaakkola, T. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. arXiv [cs.LG] (2018).