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Updated: Oct 16, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

MEMES: Machine learning framework for Enhanced MolEcular Screening.

Sarvesh Mehta¹, Siddhartha Laghuvarapu¹, Yashaswi Pathak¹

¹Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India deva@iiit.ac.in +91 40 6653 1413 +91 40 6653 1161.

Chemical Science

|October 18, 2021

Summary

This summary is machine-generated.

This study introduces a new Machine learning framework for Enhanced MolEcular Screening (MEMES) using Bayesian optimization. MEMES efficiently identifies top drug candidates from massive libraries, significantly reducing computational costs in drug discovery.

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Area of Science:

Computational Chemistry
Drug Discovery
Machine Learning

Background:

High-throughput virtual screening is crucial for identifying drug candidates.
Evaluating massive molecular libraries computationally is infeasible.
Current methods only sample a small fraction of the chemical space.

Purpose of the Study:

To develop an efficient machine learning framework for sampling chemical space.
To reduce computational costs in high-throughput screening.
To improve the identification of potential drug molecules.

Main Methods:

Proposed a novel Machine learning framework for Enhanced MolEcular Screening (MEMES).
Utilized Bayesian optimization for efficient chemical space sampling.
Applied the framework to a large molecular library.

Main Results:

Identified 90% of the top-1000 molecules from a 100-million-molecule library.
Calculated docking scores for only 6% of the complete library.
Demonstrated significant computational savings.

Conclusions:

MEMES framework offers efficient sampling of chemical space.
The approach substantially reduces computational effort in drug discovery.
Applicable to other high-throughput experimental areas.