Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
cs.LG, cond-mat.mtrl-sci, physics.chem-ph
Submitted: 2025-01-03
Updated: 2026-09-15
Code: https://github.com/chemprop/chemprop
License: http://creativecommons.org/licenses/by/4.0/
The gist: Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the
Terminology
Abstract
Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.
Sources
- MoleculeNet: A Benchmark for Molecular Machine Learning
- Neural Message Passing for Quantum Chemistry
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- Geometric Latent Diffusion Models for 3D Molecule Generation
- Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
- Inverse Design of Copolymers Including Stoichiometry and Chain Architecture
- Generative Active Learning for the Search of Small-molecule Protein Binders
- Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
- Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
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