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Found 8 papers, 1 papers with code
Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints
Accurately predicting drug-drug interactions (DDIs) is crucial for pharmaceutical research and clinical safety.
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
RNA is a vital biomolecule with numerous roles and functions within cells, and interest in targeting it for therapeutic purposes has grown significantly in recent years.
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
Scoreformer: A Surrogate Model For Large-Scale Prediction of Docking Scores
In this study, we present ScoreFormer, a novel graph transformer model designed to accurately predict molecular docking scores, thereby optimizing high-throughput virtual screening (HTVS) in drug discovery.
Are Protein Language Models Compute Optimal?
While protein language models (pLMs) have transformed biological research, the scaling laws governing their improvement remain underexplored.
TopoBench: A Framework for Benchmarking Topological Deep Learning
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL).
GeoDirDock: Guiding Docking Along Geodesic Paths
This work introduces GeoDirDock (GDD), a novel approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions.
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks
To improve the applicability domain of GM methods, we have developed a workflow based on a variational autoencoder coupled with active learning steps.
