1 citations · 1 across the 4 of their papers we have counts for
5 papers
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
Hannah Lawrence, Bodhi P. Vani, Ji Won Park +5
Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, practical use cases require a…
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…
Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignment
Ameya Daigavane, YuQing Xie, Bodhi P. Vani +3
Diffusion models are a popular class of generative models trained to reverse a noising process starting from a target data distribution. Training a diffusion model consists of lear…
JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
Ameya Daigavane, Bodhi P. Vani, Darcy Davidson +3
Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Curre…
Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE
Bodhi P. Vani, Akashnathan Aranganathan, Pratyush Tiwary
Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular cancers. The ubiquitousness and structural s…