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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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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Rohan Gupta¹, Devesh Srivastava¹, Mehar Sahu¹

¹Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.

Molecular Diversity

|April 12, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) accelerate drug discovery by overcoming traditional challenges like cost and time. These technologies, including deep learning, enhance various stages from molecule design to clinical development.

Keywords:

Artificial intelligence Artificial neural networks Computer-aided drug design Deep learning Drug design and discovery Drug repurposing Machine learning Quantitative structure–activity relationship Virtual screening

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Area of Science:

Drug discovery and development
Computational chemistry
Bioinformatics

Background:

Traditional drug design faces hurdles including low efficacy, high cost, time consumption, and complex data from genomics and proteomics.
These challenges impede efficient drug discovery pipelines, necessitating innovative approaches.

Purpose of the Study:

To highlight the pivotal role of artificial intelligence (AI) and machine learning (ML) in revolutionizing drug design and development.
To showcase how AI/ML algorithms address inefficiencies and accelerate the discovery of novel therapeutics.

Main Methods:

Application of AI and ML algorithms, including deep learning, artificial neural networks, and support vector machines.
Utilizing AI for diverse drug discovery processes: virtual screening, toxicity prediction, quantitative structure-activity relationship (QSAR) modeling, and drug repositioning.
Leveraging data mining and curation techniques to support advanced modeling algorithms.

Main Results:

AI and ML have modernized drug discovery, improving processes from peptide synthesis to molecular docking and clinical development.
These technologies enable rational drug design, enhancing prediction of bioactivity, toxicity, and drug release.
AI facilitates efficient analysis of complex biological data for biomarker development and pharmaceutical manufacturing.

Conclusions:

AI and ML offer significant opportunities to overcome traditional drug design limitations, reducing time and cost.
The integration of AI in computer-aided drug design promises more efficient and effective therapeutic development.
Advancements in AI/ML are crucial for the future of rational drug design, ultimately benefiting human health.