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Optimizing Diffusion Models for Biological Applications: A Deep Dive into Reinforcement Learning-based Fine-Tuning
ByNeermita Bhattacharya July 29, 2024October 22, 2026

Diffusion models are versatile in generative modeling but need fine-tuning for specific applications in Biology to optimize downstream reward functions. Diffusion models are known to provide excellent generative modeling capability, but practical applications require generating samples that maximize some desired metric (such as docking score in molecules and stability in proteins). Researchers from Genentech, Princeton…

Read More Optimizing Diffusion Models for Biological Applications: A Deep Dive into Reinforcement Learning-based Fine-Tuning
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending

Can Deep Learning Methods Tackle Practical Protein-Ligand Docking Challenges? Introducing PoseBench
ByAnchal Negi May 27, 2024October 22, 2026

Scientists from the University of Missouri, Columbia introduced PoseBench, the first exhaustive benchmark for practical protein-ligand docking, enabling rigorous evaluation of deep learning docking methods on predicted protein structures, multiple concurrently binding ligands, and targets without known binding pockets. It introduces multi-ligand benchmark datasets and reveals limitations of current deep learning approaches in generalizing to…

Read More Can Deep Learning Methods Tackle Practical Protein-Ligand Docking Challenges? Introducing PoseBench
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending

Transforming Fragment-based Drug Design: Stanford’s FRAME Employs Machine Learning to Expand Fragments into Ideal Ligands
BySwasti Tyagi December 1, 2023October 22, 2026

Stanford University researchers developed FRAME (Fragment-Based Molecular Expansion), a machine learning framework that uses three-dimensional structures of interactions between proteins and ligands to facilitate drug design based on ‘fragments.’ Fragments are singular chemical groups that are added to ligands for the purpose of performing lead optimization tasks and improving their properties. Ligands are small molecules…

Read More Transforming Fragment-based Drug Design: Stanford’s FRAME Employs Machine Learning to Expand Fragments into Ideal Ligands

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