Language models in molecular discovery

Abstract

The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery.

arXiv commentUnder review

References in corpus (9)

• Automatic chemical design using a data-driven continuous representation of molecules
• Deep Reinforcement Learning for De-Novo Drug Design
• Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
• GuacaMol: Benchmarking Models for De Novo Molecular Design
• Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
• Towards artificial general intelligence via a multimodal foundation model
• Keeping it Simple: Language Models can learn Complex Molecular Distributions
• Regression Transformer: Concurrent sequence regression and generation for molecular language modeling
• Accelerating Material Design with the Generative Toolkit for Scientific Discovery