Novel Ligand

Novel ligand discovery focuses on identifying small molecules that bind strongly and specifically to target proteins, a crucial step in drug development. Current research heavily utilizes machine learning, employing diverse architectures like convolutional neural networks, graph neural networks, and diffusion models to predict binding affinity, optimize ligand properties, and even generate novel ligand structures directly from protein pocket information. These advancements significantly accelerate the drug discovery process, improving efficiency and potentially leading to more effective therapeutics. The field also emphasizes improving the accuracy and generalizability of these models, particularly for novel protein targets and ligands, and incorporating protein flexibility into binding predictions.

Papers

January 25, 2023

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
Wengong Jin, Siranush Sarkizova, Xun Chen, Nir Hacohen, Caroline Uhler
Unsupervised Setting Drug Discovery Protein Ligand Affinity Prediction Novel Ligand Protein Ligand Binding Affinity Prediction Dataset Affinity

December 23, 2022

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction
Gregory W. Kyro, Rafael I. Brent, Victor S. Batista
Efficient Hybrid ATAC Net Affinity Prediction Novel Ligand Protein Engineering

November 9, 2022

Semi-Equivariant Continuous Normalizing Flows for Target-Aware Molecule Generation
Eyal Rozenberg, Daniel Freedman
Conditional Generative Molecule Generation Novel Ligand Conditional Model Candidate Molecule

October 24, 2022

Structure-based Drug Design with Equivariant Diffusion Models
Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia
Molecular Design Structure Based Drug Design Drug Like Novel Ligand Equivariant Diffusion

October 12, 2022

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong, Jian Tang
Equivariant Network Molecular Docking Protein Ligand Protein Structure Prediction Novel Ligand

June 23, 2022

CoSP: Co-supervised pretraining of pocket and ligand
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li
Molecular Property Prediction Negative Sampling Virtual Screening Protein Ligand Novel Ligand Co Training Pre Training Task New POCKET

May 5, 2022

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands
Chenru Duan, Adriana J. Ladera, Julian C. -L. Liu, Michael G. Taylor, Isuru R. Ariyarathna, Heather J. Kulik
Novel Ligand Transition Metal Transferable Time Series Molecular Machine

February 7, 2022

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi Jaakkola
Drug Discovery Geometric Deep Learning Virtual Screening Drug Like Drug Target Novel Ligand

December 25, 2021

AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
Ayan Chatterjee, Robin Walters, Zohair Shafi, Omair Shafi Ahmed, Michael Sebek, Deisy Gysi, Rose Yu, Tina Eliassi-Rad, Albert-László Barabási, Giulia Menichetti
Drug Discovery Protein Ligand Drug Target Interaction Novel Ligand Protein Target Protein Graph

November 30, 2021

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
Carter Knutson, Mridula Bontha, Jenna A. Bilbrey, Neeraj Kumar
Graph Neural Network Protein Ligand Novel Ligand Molecular Interaction Scalable Graph Neural Network