Drug Discovery Process

Drug discovery aims to identify and develop new therapeutic agents efficiently and effectively. Current research heavily utilizes machine learning, particularly deep learning models like graph neural networks and generative adversarial networks (GANs), alongside evolutionary algorithms and large language models (LLMs), to predict drug-target interactions, generate novel molecules with desired properties, and optimize existing compounds. These computational approaches are integrated with knowledge graphs and various data sources to improve prediction accuracy, reduce experimental costs, and accelerate the overall drug development process. The ultimate goal is to improve the success rate of clinical trials and provide patients with safer and more effective treatments.

Papers

November 30, 2022

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs
Pietro Bongini, Elisa Messori, Niccolò Pancino, Monica Bianchini
Graph Neural Network Human Prediction Deep Learning Approach Molecular Graph Drug Discovery Process Side Effect Graph Level Classification

November 6, 2022

Recent Developments in Structure-Based Virtual Screening Approaches
Christoph Gorgulla
Drug Discovery Small Molecule Drug Discovery Process Recent Development Based Virtual Screening

October 31, 2022

Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery
Ya Ju Fan, Jonathan E. Allen, Kevin S. McLoughlin, Da Shi, Brian J. Bennion, Xiaohua Zhang, Felice C. Lightstone
Neural Network Uncertainty Quantification Drug Discovery Predictive Uncertainty Drug Discovery Process

August 19, 2022

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
Yaosen Min, Ye Wei, Peizhuo Wang, Xiaoting Wang, Han Li, Nian Wu, Stefan Bauer, Shuxin Zheng, Yu Shi, Yingheng Wang, Ji Wu, Dan Zhao, Jianyang Zeng
Structure Based Drug Design Protein Ligand Virtual Screening Affinity Prediction Drug Discovery Process Structural Dynamic Graph Based Deep Learning Binding Affinity

August 9, 2022

Comparison of semi-supervised learning methods for High Content Screening quality control
Umar Masud, Ethan Cohen, Ihab Bendidi, Guillaume Bollot, Auguste Genovesio
Transfer Learning Semi Supervised Learning Consistent Comparison Early Screening Drug Discovery Process Quality Control Quality Text Artefact Detection

June 20, 2022

SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction
Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu, Rui Yan
Drug Discovery Data Scarcity Knowledge Barrier Drug Feature Drug Target Drug Discovery Process Drug Target Affinity Prediction

February 15, 2022

Modular multi-source prediction of drug side-effects with DruGNN
Pietro Bongini, Franco Scarselli, Monica Bianchini, Giovanna Maria Dimitri, Niccolò Pancino, Pietro Liò
Graph Neural Network Drug Discovery Modular System Drug Discovery Process Side Effect

December 27, 2021

ToxTree: descriptor-based machine learning models for both hERG and Nav1.5 cardiotoxicity liability predictions
Issar Arab, Khaled Barakat
Feature Descriptor Drug Discovery Process Quantitative Structure Activity Relationship Sodium Channel

Drug Discovery Process

Papers

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs

Recent Developments in Structure-Based Virtual Screening Approaches

Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning

Comparison of semi-supervised learning methods for High Content Screening quality control

SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction

Modular multi-source prediction of drug side-effects with DruGNN

ToxTree: descriptor-based machine learning models for both hERG and Nav1.5 cardiotoxicity liability predictions