Optimizing molecules for desired properties is a fundamental yet challenging
task in chemistry, material science, and drug discovery. This paper develops
a novel algorithm for optimizing molecular properties via an Expectation-
Maximization (EM) like explainable evolutionary process. The algorithm is designed
to mimic human experts in the process of searching for desirable molecules
and alternate between two stages: the first stage on explainable local search which
identifies rationales, i.e., critical subgraph patterns accounting for desired molecular
properties, and the second stage on molecule completion which explores the
larger space of molecules containing good rationales. We test our approach against
various baselines on a real-world multi-property optimization task where each
method is given the same number of queries to the property oracle. We show
that our evolution-by-explanation algorithm is 79% better than the best baseline
in terms of a generic metric combining aspects such as success rate, novelty, and
diversity. Human expert evaluation on optimized molecules shows that 60% of
top molecules obtained from our methods are deemed successful.
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Multi-Objective Molecule Generation using Interpretable Substructures
Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection of multiple property constraints. This task becomes increasingly challenging when there are many property constraints. We propose to offset this complexity by composing molecules from a vocabulary of substructures that we call molecular rationales. These rationales are identified from molecules as substructures that are likely responsible for each property of interest. We then learn to expand rationales into a full molecule using graph generative models. Our final generative model composes molecules as mixtures of multiple rationale completions, and this mixture is fine-tuned to preserve the properties of interest. We evaluate our model on various drug design tasks and demonstrate significant improvements over state-of-the-art baselines in terms of accuracy, diversity, and novelty of generated compounds.
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- Award ID(s):
- 1918839
- PAR ID:
- 10219628
- Date Published:
- Journal Name:
- Proceedings of the 37th International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Generative models are a sub-class of machine learning models that are capable of generating new samples with a target set of properties. In chemical and materials applications, these new samples might be drug targets, novel semiconductors, or catalysts constrained to exhibit an application-specific set of properties. Given their potential to yield high-value targets from otherwise intractable design spaces, generative models are currently under intense study with respect to how predictions can be improved through changes in model architecture and data representation. Here we explore the potential of multi-task transfer learning as a complementary approach to improving the validity and property specificity of molecules generated by such models. We have compared baseline generative models trained on a single property prediction task against models trained on additional ancillary prediction tasks and observe a generic positive impact on the validity and specificity of the multi-task models. In particular, we observe that the validity of generated structures is strongly affected by whether or not the models have chemical property data, as opposed to only syntactic structural data, supplied during learning. We demonstrate this effect in both interpolative and extrapolative scenarios (i.e., where the generative targets are poorly represented in training data) for models trained to generate high energy structures and models trained to generated structures with targeted bandgaps within certain ranges. In both instances, the inclusion of additional chemical property data improves the ability of models to generate valid, unique structures with increased property specificity. This approach requires only minor alterations to existing generative models, in many cases leveraging prediction frameworks already native to these models. Additionally, the transfer learning strategy is complementary to ongoing efforts to improve model architectures and data representation and can foreseeably be stacked on top of these developments.
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Accepted Manuscript1.0
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- The DOI is not currently available.
