International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Bioactivity prediction plays a crucial role in contemporary drug discovery, allowing researchers to efficiently pinpoint potential therapeutic candidates while minimizing both experimental costs and development timelines. This paper offers an in-depth exploration of machine learning techniques aimed at forecasting the biological activities of chemical substances against specific biological targets. We evaluate a range of algorithmic methods, including Random Forest, Support Vector Machines, Neural Networks, and Bayesian techniques, assessing their effectiveness across comprehensive datasets.

Additionally, the study delves into molecular representation methods, feature engineering tactics, and validation frameworks that are vital for creating reliable bioactivity prediction models. Our findings reveal that machine learning methodologies can deliver remarkable predictive accuracy, with certain algorithms outperforming others based on the characteristics of the dataset.

We also examine the integration of extensive databases such as ChEMBL and PubChem, which provide crucial training data for crafting adaptable models. The results underscore both the transformative capabilities and existing challenges of computational bioactivity prediction while offering insights into future research avenues such as explainable AI, transfer learning, and multi-omics integration. This research adds to the accumulating evidence that positions machine learning as an essential resource for expediting pharmaceutical research and lessening reliance on expensive high-throughput screening experiments.

Keywords: Bioactivity prediction, Machine learning, Drug discovery, QSAR, Molecular descriptors, ChEMBL, Deep learning, Random Forest.

Downloads: | DOI: 10.17148/IJARCCE.2025.141255