37 citations · 173 across the 23 of their papers we have counts for
8 papers · 1 filter
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen +4
Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diver…
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy +4
We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models while incurring minimum los…
AlphaFold Distillation for Protein Design
Igor Melnyk, Aurelie Lozano, Payel Das +1
Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on ex…
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
Brian Belgodere, Vijil Chenthamarakshan, Payel Das +9
With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based rea…
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Matteo Manica, Jannis Born, Joris Cadow +21
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful represe…
Learning Geometrically Disentangled Representations of Protein Folding Simulations
N. Joseph Tatro, Payel Das, Pin-Yu Chen +2
Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics. This work focuses on learning a generative…
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations
Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar +4
Explainable ML for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experiment…
Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets
Vijil Chenthamarakshan, Samuel C. Hoffman, C. David Owen +17
The discovery of novel inhibitor molecules for emerging drug-target proteins is widely acknowledged as a challenging inverse design problem: Exhaustive exploration of the vast chem…