Abstract
Background: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results: The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion: With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.
Original language | English |
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Article number | 338 |
Journal | BMC Bioinformatics |
Volume | 22 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2021 |
Funding
We thank Wendy Cornell for suggestions and discussions on the topic of drug discovery. We thank Josef Klucik and Paul Winget for the discussions on the subject of the sweeteners. R.Z. and G.C. gratefully acknowledge the financial support from the IBM Bluegene Science Program (W125859, W1464125 and W1464164), Computing Cloud Clusters and Witherspoon supercomputer in IBM.
Funders | Funder number |
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IBM BlueGene Science Program | W1464164, W1464125, W125859 |
International Business Machines Corporation |
Keywords
- Clustering
- Drug discovery
- Lead optimization
- Machine learning
- Molecular dynamics simulation
- Variational autoencoder