6 papers
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,…
Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction
Sepideh Maleki, Jan-Christian Huetter, Kangway V. Chuang +3
Predicting transcriptional responses to novel drugs provides a unique opportunity to accelerate biomedical research and advance drug discovery efforts. However, the inherent comple…
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,…