Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Abstract

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2’s active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.

Multi‐Technique Approach to Identify Potent Antimicrobial Agents from Calotropis procera: Insight into Pharmacophore Modeling, Molecular docking, MD simulation, and DFT Approaches

“…Molecular dynamics simulations were conducted for each complex, encompassing 3VOB-Caryophyllene oxide and 6F86-Stigmasterol, employing AMBER22 with the FF19SB force field. [25,26] Comprehensive system setup and simulation details were adapted from the protocols outlined by shahab et al in 2021. [27] Initially, the systems were solvated in an OPC water box, and Na + ions were added for neutralization.…”

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction

“…However, the label acquirement (i.e., molecules with known property) is a tough and expensive process. On the other hand, there are many databases with a large amount of data but no label information (e.g., ZINC [20], ChEMBL, and PubChem). How to reasonably and effectively utilize these data to improve the accuracy of molecular property prediction is an open problem to be solved.…”

A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites

“…In recent years, machine learning (ML) techniques have been widely adopted in relevant fields of biochemistry [15], material science [16,17], and mechanical performance analysis [18,19]. with an end-to-end prediction paradigm, using a simulated dataset is commonly reported [19,20].…”

“…Accuracy comparison between the EML model and the three base learners on the training set and test set. ×1015 2.360 ×1016 7.629 ×1015 2.050 ×1016 3.538 ×1015 1.450 ×1016 2.545 ×1015 1.260 ×1016 …”