19 citations · 33 across the 7 of their papers we have counts for
7 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,…
Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
Karina Zadorozhny, Kangway V. Chuang, Bharath Sathappan +3
Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Re…
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion
Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant +3
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformat…
CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
Colin A. Grambow, Hayley Weir, Christian N. Cunningham +2
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However,…
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction
Gabriele Scalia, Colin A. Grambow, Barbara Pernici +2
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with gra…