1.2k citations · 1.2k across the 9 of their papers we have counts for
10 papers
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland +6
Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints. We pre…
BASIL: Bayesian Application for Scientific Iteration and Learning
Kelvin P. Idanwekhai, Valeriia Kaneva, Stefano Menegatti +1
We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition functions that can be used to…
KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening
Pavel Koptev, Nikita Krainov, Konstantin Malkov +1
Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In the…
Binding Free Energies without Alchemy
Michael Brocidiacono, Brandon Novy, Rishabh Dey +2
Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by t…
Extending machine learning model for implicit solvation to free energy calculations
Rishabh Dey, Michael Brocidiacono, Kushal Koirala +2
The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to…
Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Michael Brocidiacono, James Wellnitz, Konstantin I. Popov +1
Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for nov…
Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching
Yunni Qu, Bhargav Vaduri, Karthikeya Jatoth +6
Machine learning (ML) models are increasingly deployed for virtual screening in drug discovery, where the goal is to identify novel, chemically diverse scaffolds while minimizing e…
An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
Michael Brocidiacono, Konstantin I. Popov, Alexander Tropsha
Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery. SBVS models are assessed by measuring the enrichment of known active molecules over deco…
Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search
Kathryn E. Kirchoff, James Wellnitz, Joshua E. Hochuli +4
Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task st…
Deep Reinforcement Learning for De-Novo Drug Design
Mariya Popova, Olexandr Isayev, Alexander Tropsha
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep an…