Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Pathway-Aware Template-Based Retrosynthesis.

Journal of chemical information and modeling·2026

Same author

Quantifying the failure modes of current one-step retrosynthesis models.

Chemical science·2026

Same author

A machine learning-based workflow for transaminase selection.

Chemical science·2026

Same author

An overview of reaction outcome prediction with physics-based and data-driven methods.

Chemical Society reviews·2026

Same author

Hydrolysis Reaction Rate Prediction Using Machine Learning: WaterDRoP.

Environmental science & technology·2026

Same author

Higher-Level Strategies for Computer-Aided Retrosynthesis.

ACS central science·2026

Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026

Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026

Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026

Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026

Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026

Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 8, 2025

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Computer-aided multi-objective optimization in small molecule discovery.

Jenna C Fromer¹, Connor W Coley^1,2

¹Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA.

Patterns (New York, N.Y.)

|March 6, 2023

Summary

This summary is machine-generated.

Pareto optimization offers a superior approach to multi-objective molecular discovery compared to scalarization by revealing trade-offs without needing predefined importance. This review explores generative methods and Bayesian optimization for enhanced molecular design.

More Related Videos

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

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

Related Experiment Videos

Last Updated: Aug 8, 2025

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

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
Drug Discovery
Machine Learning

Background:

Molecular discovery involves optimizing multiple competing properties, a complex challenge in computational chemistry.
Traditional scalarization methods for multi-objective molecular design impose assumptions and obscure objective trade-offs.

Purpose of the Study:

To review generative approaches for multi-objective molecular discovery, focusing on Pareto optimization algorithms.
To highlight the advantages of Pareto optimization over scalarization in revealing molecular property trade-offs.

Main Methods:

Discussion of pool-based and de novo generative strategies for molecular design.
Exploration of how generative models extend to multi-objective optimization using techniques like non-dominated sorting.
Examination of Bayesian optimization's role in multi-objective molecular discovery.

Main Results:

Pareto optimization provides a more comprehensive understanding of molecular property trade-offs than scalarization.
Pool-based molecular discovery is a direct extension of multi-objective Bayesian optimization.
Generative models can be adapted for multi-objective optimization through various algorithmic modifications.

Conclusions:

Pareto optimization is a powerful framework for multi-objective molecular discovery.
Integrating Bayesian optimization techniques into multi-objective de novo design presents significant opportunities for future research.