In-silico design strategies for tubulin inhibitors for the development of anticancer therapies

Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, necessitating the quest for safer alternatives. The authors explore various aspects of computer-aided drug design (CADD) for tubulin inhibitors. The authors review various techniques like molecular docking, QSAR analysis, molecular dynamic simulations, and machine learning approaches for predicting drug efficacy and modern computational methods utilized in the design and discovery of agents with anticancer potential. This article is based on a comprehensive search of literature utilizing Scopus, PubMed, Google Scholar, and Web of Science, covering the period from 2018 to 2025. CADD is crucial in the pursuit of new cancer treatments, particularly by merging computer algorithms with experimental data. CADD predicts small molecule activity against tubulin related targets, expediting drug candidate identification and optimization for enhanced efficacy with reduced toxicity. Challenges include limited predictive models and the need for sophisticated ones to capture complex interactions among targets and pathways. Despite relying on cancer cell line transcriptome profiles, CADD remains pivotal for future anticancer drug discovery efforts.