Machine Learning-Driven Drug Discovery: Prediction of Structure-Cytotoxicity Correlation Leads to Identification of Potential Anti-Leukemia Compounds

Zishen Li, Yun Wah Lam, Qi Liu, Alison Y.K. Lau, Ho Yu Au-Yeung, Rosa H.M. Chan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In vitro cytotoxicity screening is a crucial step of anticancer drug discovery. The application of deep learning methodology is gaining increasing attentions in processing drug screening data and studying anticancer mechanisms of chemical compounds. In this work, we explored the utilization of convolutional neural network in modeling the anticancer efficacy of small molecules. In particular, we presented a VGG19 model trained on 2D structural formulae to predict the growth-inhibitory effects of compounds against leukemia cell line CCRF-CEM, without any use of chemical descriptors. The model achieved a normalized RMSE of 15.76% on predicting growth inhibition and a Pearson Correlation Coefficient of 0.72 between predicted and experimental data, demonstrating a strong predictive power in this task. Furthermore, we implemented the Layer-wise Relevance Propagation technique to interpret the network and visualize the chemical groups predicted by the model that contribute to toxicity with human-readable representations.Clinical relevance - This work predicts the cytotoxicity of chemical compounds against human leukemic lymphoblast CCRF-CEM cell lines on a continuous scale, which only requires 2D images of the structural formulae of the compounds as inputs. Knowledge in the structure-toxicity relationship of small molecules will potentially increase the hit rate of primary drug screening assays.

Original languageEnglish
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5464-5467
Number of pages4
ISBN (Electronic)9781728119908
ISBN (Print)9781728119915
DOIs
Publication statusPublished - 27 Aug 2020
Externally publishedYes
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020
Conference number: 42

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PublisherIEEE
Volume2020-July
ISSN (Print)2375-7477
ISSN (Electronic)2694-0604

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2020
Country/TerritoryCanada
CityMontreal
Period20/07/2024/07/20

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