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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Large Scale Zebrafish-Based In vivo Small Molecule Screen

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Published on: December 30, 2010

DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening.

Fangping Wan¹, Yue Zhu², Hailin Hu³

¹Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.

Genomics, Proteomics & Bioinformatics

|February 9, 2020

Summary

This summary is machine-generated.

DeepCPI, a novel deep learning framework, accurately predicts compound-protein interactions (CPIs) by learning features from large unlabeled datasets. This tool aids drug discovery and repositioning by identifying potential drug-target relationships.

Keywords:

Compound–protein interaction prediction Deep learning Drug discovery In silico drug screening Machine learning

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Last Updated: Dec 29, 2025

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Published on: December 30, 2010

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

Computational chemistry
Bioinformatics
Drug discovery

Background:

Accurate prediction of compound-protein interactions (CPIs) is crucial for understanding drug mechanisms and accelerating drug discovery.
Existing computational methods often fail to leverage large unlabeled datasets and are limited in scale.

Purpose of the Study:

To develop a novel, scalable computational framework (DeepCPI) for accurate, large-scale prediction of CPIs.
To address limitations of conventional similarity- or docking-based methods.

Main Methods:

DeepCPI utilizes representation learning for effective feature embedding of compounds and proteins.
Combines feature embedding with deep learning for large-scale CPI prediction.
Learns implicit, low-dimensional features from massive unlabeled data.

Main Results:

DeepCPI demonstrated superior predictive performance on large-scale databases (ChEMBL, BindingDB) and DrugBank.
Experimentally validated several predicted interactions between small molecules and G protein-coupled receptors (GPCRs).

Conclusions:

DeepCPI is a powerful and scalable tool for predicting compound-protein interactions.
The framework facilitates drug discovery and drug repositioning efforts.
Source code is publicly available for further research and application.