Large property models: a new generative machine-learning formulation for molecules

Abstract

Generative models for the inverse design of molecules with particular properties have been heavily hyped, but have yet to demonstrate significant gains over machine-learning-augmented expert intuition. A major challenge of such models is their limited accuracy in predicting molecules with targeted properties in the data-scarce regime, which is the regime typical of the prized outliers that it is hoped inverse models will discover. For example, activity data for a drug target or stability data for a material may only number in the tens to hundreds of samples, which is insufficient to learn an accurate and reasonably general property-to-structure inverse mapping from scratch. We’ve hypothesized that the property-to-structure mapping becomes unique when a sufficient number of properties are supplied to the models during training. This hypothesis has several important corollaries if true. It would imply that data-scarce properties can be completely determined using a set of more accessible molecular properties. It would also imply that a generative model trained on multiple properties would exhibit an accuracy phase transition after achieving a sufficient size—a process analogous to what has been observed in the context of large language models. To interrogate these behaviors, we have built the first transformers trained on the property-to-molecular-graph task, which we dub “large property models” (LPMs). A key ingredient is supplementing these models during training with relatively basic but abundant chemical property data. The motivation for the large-property-model paradigm, the model architectures, and case studies are presented here.

Graphical abstract: Large property models: a new generative machine-learning formulation for molecules

Article information

Article type
Paper
Submitted
27 May 2024
Accepted
29 Jul 2024
First published
27 Sep 2024
This article is Open Access
Creative Commons BY license

Faraday Discuss., 2024, Advance Article

Large property models: a new generative machine-learning formulation for molecules

T. Jin, V. Singla, H. Hsu and B. M. Savoie, Faraday Discuss., 2024, Advance Article , DOI: 10.1039/D4FD00113C

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