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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

Leili Zhang¹, Giacomo Domeniconi², Chih-Chieh Yang³

¹IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA. zhangle@us.ibm.com.

BMC Bioinformatics

|June 23, 2021

Summary

This summary is machine-generated.

This study introduces a novel in silico approach combining machine learning and molecular modeling to automate drug lead optimization. The method identifies modification hotspots, reducing costs and time in drug discovery.

Keywords:

Clustering Drug discovery Lead optimization Machine learning Molecular dynamics simulation Variational autoencoder

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Area of Science:

Computational Chemistry
Drug Discovery
Machine Learning

Background:

Drug discovery involves costly pre-clinical research and clinical trials, with lead optimization consuming over half the pre-clinical budget.
Current lead optimization processes are time-consuming and expensive, necessitating more efficient methodologies.

Purpose of the Study:

To develop a computational method for partially automating the lead optimization workflow.
To identify potential modification hotspots in drug candidates using machine learning and molecular modeling.

Main Methods:

Utilizing physics-based molecular dynamics simulations for initial data collection.
Extracting preliminary features using contact matrices and enhancing them with temporal dynamism representation.
Modeling the enhanced data with an unsupervised convolutional variational autoencoder (CVAE).
Comparing conventional and CVAE-based clustering methods to rank submolecular structures.

Main Results:

The CVAE model effectively processed temporal simulation data for enhanced feature representation.
Clustering methods identified and ranked submolecular structures, proposing potential candidates for lead optimization.
The approach successfully generated suggestions for drug modification hotspots.

Conclusions:

The proposed method offers a way to identify drug modification hotspots without extensive structure-activity data.
This approach can improve drug potency and significantly reduce lead optimization time.
The tool has the potential to become valuable for medicinal chemists in accelerating drug discovery.