From prediction to design: A machine learning model for de novo bioactive compounds and personalized biotherapeutics

Traditional approaches for identifying and improving bioactive molecules, important for pharmaceutical development, are laborious and inefficient. Therapeutic chemical screening, development, and customization are now faster and more accurate through machine learning (ML). This review summarizes ML-driven bioactive chemical research and how it is altering customized medicine. Recent research suggests that deep neural models excel in molecular bioactivity forecasting, with AUC-ROC values ranging from 0.90 to 0.96 and mean R² values exceeding 0.85 on independent test sets, resulting in 20–30 % fewer false positives than traditional QSAR methods. ML-driven optimization pipelines have accelerated early drug discovery by 40–60 % by reducing lead identification times from several years to less than 24 months in certain industries. This proof supports the assumption that machine learning aids precision treatments and bioactive chemical production. ML models guess molecular properties, improve pharmacokinetic profiles, and create new scaffolds from scratch using large chemical and biological datasets, different ways of representing molecules, and advanced algorithms, from supervised and unsupervised learning to the latest deep generative models. This study explored ML in personalized medicine, including predictive biomarker identification, genotype-specific medication optimization, and nutraceutical modification for rare diseases. In addition to data quality and model interpretability challenges, this study examined the complex ethical and legal issues of data privacy, fair access, and biosecurity. Finally, this study discussed how multi-omics data, quantum machine learning, and synthetic biology could enable tailored, on-demand medical therapies in the future. Machine learning is essential for targeted, efficient, and highly tailored healthcare.