Artificial Intelligence in Drug Discovery: A Data- Driven Methodological Framework

Artificial Intelligence (AI) is transforming the traditional drug discovery pipeline by
accelerating target identification, compound screening, and optimization. Conventional drug
development is often time-consuming and costly, typically requiring more than a decade of
research and billions of dollars in investment. Recent advancements in machine learning,
deep learning, and large-scale biological datasets enable predictive modeling that can
significantly reduce this timeline. This study presents a conceptual framework for integrating
AI techniques into early-stage drug discovery. Using publicly available biomedical datasets,
machine learning models can analyze molecular structures, predict drug–target interactions,
and prioritize promising compounds for experimental validation.The framework highlights the
potential of AI to improve efficiency, reduce development costs, and identify novel
therapeutic candidates. The results demonstrate that the AI-driven computational screening
can complement the laboratory experiments and facilitate it faster translation from research .

The methodology follows a computational research pipeline commonly presented in AI-
healthcare conferences. First, biological and chemical datasets are collected from public

databases such as molecular structure repositories and genomic datasets. Second, data
preprocessing techniques including normalization, feature engineering, and molecular fingerprint
extraction are applied to prepare the data for machine learning models. Third, predictive
algorithms such as Random Forest, Support Vector Machines, and Deep Neural Networks are
trained to predict drug-target interactions and biological activity. Finally, the top-ranked
candidate molecules are identified for further in-silico simulation or laboratory validation.

Keywords
Machine Learning, Deep Learning, Drug-Target Interaction, Computational Biology, Bioinformatics