11 citations · 23 across the 16 of their papers we have counts for
5 papers · 1 filter
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragme…
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Peter St. John, Dejun Lin, Polina Binder +89
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…
Want to train KANS at scale? Now UKAN!
Alireza Moradzadeh, Srimukh Prasad Veccham, Lukasz Wawrzyniak +2
Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional multilayer perceptrons. However, their reliance on predefined, bounded grids restri…
DualBind: A Dual-Loss Framework for Protein-Ligand Binding Affinity Prediction
Meng Liu, Saee Gopal Paliwal
Accurate prediction of protein-ligand binding affinities is crucial for drug development. Recent advances in machine learning show promising results on this task. However, these me…
LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
Niraj Kumar-Singh, Gustavo Polleti, Saee Paliwal +1
While there are a plethora of methods for link prediction in knowledge graphs, state-of-the-art approaches are often black box, obfuscating model reasoning and thereby limiting the…