TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors. (15th October 2020)

• Record Type: Journal Article
• Authors: Saini, Ravi; Fatima, Shehnaz; Agarwal, Subhash Mohan
• Is Part Of: Chemical biology & drug design. Volume 96:Number 3(2020)
• Page Start: 921
• Page End: 930
• Publication Date: 2020-10-15
• Subjects: classification models -- EGFR -- machine learning -- random forest -- T790M; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.13697
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract: The EGFR is a clinically important therapeutic drug target in lung cancer. The first‐generation tyrosine kinase inhibitors used in clinics are effective against L858R‐mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance‐based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross‐validation and independent validation datasets. It was observed that the random forest‐based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open‐source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti‐TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants. Abstract : The development of new inhibitors against the resistant double‐mutated EGFR is a clinical necessity. Therefore, machine learning‐based classification models have been developed for identifying TMLR inhibitors. The best performing models have been incorporated into an application termed TMLRpred, which can be used for prescreening and identifying compounds that could be active against the double‐mutated EGFR.