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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...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...

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Updated: Nov 21, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Towards reproducible computational drug discovery.

Nalini Schaduangrat¹, Samuel Lampa², Saw Simeon³

¹Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.

Journal of Cheminformatics

|January 12, 2021

Summary

This summary is machine-generated.

Reproducible computational drug discovery enhances scientific progress by standardizing methods and promoting open data/code sharing. This approach is crucial for reliable research and collaborative innovation in drug design.

Keywords:

Bioinformatics Cheminformatics Data science Data sharing Drug design Drug discovery Open data Open science Reproducibility Reproducible research

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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 science
Drug discovery
Reproducible research

Background:

Reproducibility is a significant challenge in scientific research.
Computational methods are vital in drug discovery for data handling and analysis.
Lack of reproducibility hinders scientific advancement.

Purpose of the Study:

To provide a comprehensive review of reproducible research in computational drug discovery.
To explore current methodologies and challenges in ensuring reproducibility.
To highlight the importance of open data and code sharing.

Main Methods:

Review of state-of-the-art reproducible research practices.
Analysis of research documentation tools (e.g., Jupyter notebooks).
Comparison of reproducibility with related concepts (replicability, reusability, reliability).
Examination of model development and deployment in computational drug discovery.
Discussion of computational challenges and use cases.

Main Results:

Computational drug discovery relies heavily on data collection, pre-processing, analysis, and inference.
Sharing data and code is a common practice in computational fields to ensure reproducibility and foster collaboration.
An open approach to data/code collection, curation, and sharing is essential for computational drug design.

Conclusions:

Adopting reproducible practices is inevitable for the field of computational drug design.
Openness in data and code sharing accelerates scientific progress and collaboration.
Standardized computational protocols are key to streamlining drug discovery efforts.