(429d) QSAR Study of Combretastatin-like Chalcones As Cancer Cell Growth Inhibitors Using Linear and Non-Linear Machine Learning Approaches
This project aims at modifying, improving, and implementing machine learning and model development algorithms such as Artificial Bee Colony, Multi-gene Genetic Algorithm, Particle Swarm Optimization algorithms in combination with linear, non-linear, and LASSO regression algorithms. The goal is to develop models with better fitness using descriptors that are comparatively easier to calculate (e.g. connectivity descriptors, 2D descriptors). Avogadro open access software was used to develop and optimize the molecular structures, and Dragon 6.0 software was used to generate descriptors for the given compounds. One-fifth of the samples were used for external validation of model. Model superiority was decided based on values of R2, Q2, MSE, and MAE values of the models.
 Sharma et al, A review of mechanisms of antitumor activity of chalcones, 2016, Anticancer Agents Med Chem, 16, 200-211.
 Ducki et al, Combretastatin-like chalcoles as inhibitors of microtubule polymerization. Part 1: synthesis and biological evaluation of anitvascular activity, 2009, Bioorg Med Chem, 17, 7698-7710.
 Lippinski et al, A molecular modeling study of Combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models, 2018, Struct Chem (2018), https://doi.org/10.1007/s11224-017-1072-2