Retraining molecular property predictors on active-learning batches of DFT-computed molecules enables a genetic generative model to generate molecules with properties beyond the training data and more stable molecules.
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
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abstract
The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Surprisingly, we find that model performance can strongly depend on the benchmark domain. We believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to designing molecules that solve existing problems in both academia and industry alike.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Active Learning Enables Extrapolation in Molecular Generative Models
Retraining molecular property predictors on active-learning batches of DFT-computed molecules enables a genetic generative model to generate molecules with properties beyond the training data and more stable molecules.