Drug Discovery

Drug discovery, the process of identifying and developing new therapeutic agents, is being revolutionized by artificial intelligence. Current research focuses on improving the accuracy and efficiency of computational models for predicting molecular properties, drug-target interactions, and pharmacokinetics, employing techniques like graph neural networks, transformers, and diffusion models, often enhanced by self-supervised learning and multi-task learning strategies. These advancements aim to accelerate the lengthy and expensive drug development pipeline, ultimately leading to faster identification of effective and safer drugs. The integration of large language models and quantum computing further expands the possibilities for innovative drug design and discovery.

Papers

November 27, 2023

November 4, 2023

FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Sina Abdidizaji, Ozlem Ozmen Garibay
Drug Discovery Drug Target Interaction Protein Target Drug Target Pair

November 1, 2023

Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods
Minsi Ren, Bowen Gao, Bo Qiang, Yanyan Lan
Global Evaluation Drug Discovery Structure Based Drug Design Protein Ligand

October 30, 2023

DGFN: Double Generative Flow Networks
Elaine Lau, Nikhil Vemgal, Doina Precup, Emmanuel Bengio
Drug Discovery Generative Flow Network Energy Based GFlowNets Gabor Filter Double Deep Q Learning

October 25, 2023

Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds
Jaiveer Gill, Ratul Chakraborty, Reetham Gubba, Amy Liu, Shrey Jain, Chirag Iyer, Obaid Khwaja, Saurav Kumar
Unsupervised Learning Drug Discovery Molecular Property Prediction Known Molecule Cluster Structure Chemical Compound Emergent Property Molecular Embeddings

October 23, 2023

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing
Shunian Xiang, Patrick J. Lawrence, Bo Peng, ChienWei Chiang, Dokyoon Kim, Li Shen, Xia Ning
Drug Discovery Drug Candidate Drug Repurposing Sample Path

October 10, 2023

DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Bowen Gao, Bo Qiang, Haichuan Tan, Minsi Ren, Yinjun Jia, Minsi Lu, Jingjing Liu, Weiying Ma, Yanyan Lan
Drug Discovery Virtual Screening Binding Affinity

October 9, 2023

Transformers and Large Language Models for Chemistry and Drug Discovery
Andres M Bran, Philippe Schwaller
Large Language Model Transformer Megatron Decepticons Natural Language Transformer Architecture Drug Discovery Molecular Graph Organic Chemistry

October 6, 2023

PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps
Rakesh Bal, Yijia Xiao, Wei Wang
Drug Discovery Protein Language Model Protein Ligand Drug Target Interaction Drug Target Drug Target Interaction Prediction

October 2, 2023

September 28, 2023

Neural scaling laws for phenotypic drug discovery
Drew Linsley, John Griffin, Jason Parker Brown, Adam N Roose, Michael Frank, Peter Linsley, Steven Finkbeiner, Jeremy Linsley
Drug Discovery Neural Topic Small Molecule

September 22, 2023

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery
Alvaro Prat, Hisham Abdel Aty, Gintautas Kamuntavičius, Tanya Paquet, Povilas Norvaišas, Piero Gasparotto, Roy Tal
Deep Learning Approach Drug Discovery Structure Based Drug Design High Throughput Protein Ligand

September 21, 2023

Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
Roohallah Alizadehsani, Solomon Sunday Oyelere, Sadiq Hussain, Rene Ripardo Calixto, Victor Hugo C. de Albuquerque, Mohamad Roshanzamir, Mohamed Rahouti, Senthil Kumar Jagatheesaperumal
Comprehensive Survey Development Activity Explainable Artificial Intelligence XAI Method Drug Discovery xAI Community Toxicity Detection

September 20, 2023

Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening
Zhonglin Cao, Simone Sciabola, Ye Wang
Active Learning Drug Discovery Sample Efficiency Virtual Screening Large Scale Pretraining Compound Library Ligand Based Based Virtual Screening

September 12, 2023

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery
Ronen Taub, Yonatan Savir
Drug Discovery Known Molecule Chemical Space Boltzmann Distribution Atomic Knowledge Aggregation Strategy Improving Prediction

September 6, 2023

A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery
L. Domingo, M. Chehimi, S. Banerjee, S. He Yuxun, S. Konakanchi, L. Ogunfowora, S. Roy, S. Selvaras, M. Djukic, C. Johnson
Neural Network Quantum Machine Learning Drug Discovery Quantum Classical Affinity Prediction Quantum Classical Neural Network Candidate Molecule

September 4, 2023

NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach
Srilekha Mamidala
Drug Discovery Seizure Detection Computational Model Computer Aided Drug Discovery Drug Candidate Drug Repurposing Drug Related

August 17, 2023

Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions
Lucian Chan, Marcel Verdonk, Carl Poelking
Drug Discovery Neural Process Virtual Screening Unobserved Heterogeneity Affinity Prediction Computer Aided Drug Discovery Bioactivity Prediction Bioactivity Data

Drug Discovery

Papers

Target-Free Compound Activity Prediction via Few-Shot Learning

InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery

FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation

Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods

DGFN: Double Generative Flow Networks

Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing

DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening

Transformers and Large Language Models for Chemistry and Drug Discovery

PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps

Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction

Drug Discovery with Dynamic Goal-aware Fragments

Neural scaling laws for phenotypic drug discovery

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey

Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery

A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach

Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions