6 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…
Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings
Frank Hu, Shriram Chennakesavalu, David Graff
Frontier large language models (LLMs) have become attractive priors for optimization due to their large-scale pretraining that enables them to navigate a variety of optimization se…
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,…
Scaling Transferable Coarse-graining with Mean Force Matching
Abigail Park, Shriram Chennakesavalu, Grant M. Rotskoff
Coarse-grained molecular dynamics often sacrifices accuracy and transferability for computational efficiency, but the use of machine learned potentials is helping coarse-grained mo…
Aligning Transformers with Continuous Feedback via Energy Rank Alignment
Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran +1
Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms. Large, autoregress…
Data-efficient generation of protein conformational ensembles with backbone-to-side chain transformers
Shriram Chennakesavalu, Grant M. Rotskoff
Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models o…