4 papers
Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling
Frank Hu, Shriram Chennakesavalu, Zichen Wang +5
Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for molecules that satisfy multiple, often competing, objectives. Large langua…
SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
Miruna Cretu, John Bradshaw, Patricia Suriana +6
We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to m…
Evaluating the Progression of Large Language Model Capabilities for Small-Molecule Drug Design
Shriram Chennakesavalu, Kirill Shmilovich, Hayley Weir +5
Large Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse sources and formats. However,…
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces
Michael S. Jones, Kirill Shmilovich, Andrew L. Ferguson
Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long-time scales s…