Published Paper Details:

Bioactivity forecast may be a basic errand in sedate revelation and advancement, empowering the recognizable proof of potential medicate candidates with tall viability and negligible poisonous quality. By leveraging endless chemical datasets, ML models can learn complex structure-activity connections (SARs) and make exact expectations almost compound intelligent with natural targets Different ML strategies, counting profound learning, irregular woodlands, bolster vector machines, and gathering models, are utilized to improve prescient exactness. Also, progressions in logical AI (XAI) contribute to way better show interpretability, helping chemists in levelheaded medicate plan. This paper investigates later improvements in ML-based bioactivity forecast, challenges such as information quality and show generalizability, and future headings, counting the integration of generative AI and multi-omics information. In this field, machine learning (ML) has grown as an effective tool for promoting data-driven methods that predict the unplanned behaviour of chemical molecule.