292 citations · 709 across the 9 of their papers we have counts for
6 papers · 1 filter
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
Mayk Caldas Ramos, Andrew D. White
Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy a…
Bloom filters for molecules
Jorge Medina, Andrew D White
Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we…
ChemCrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Oliver Schilter +3
Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their fu…
Bayesian Optimization of Catalysis With In-Context Learning
Mayk Caldas Ramos, Shane S. Michtavy, Marc D. Porosoff +1
Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning. We extend this capability to regression with uncertainty est…
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
Alston Lo, Robert Pollice, AkshatKumar Nigam +3
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted in…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…