2025· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
This study reviews AI-driven drug discovery methods, presents a structured AI pipeline from data collection to candidate selection, and evaluates performance using metrics such as prediction accuracy, screening efficiency, lead optimization success, and toxicity reduction.
Abstract
Artificial Intelligence (AI) is transforming drug discovery by making the process faster, more cost-effective, and more accurate than traditional methods, which often require 10–15 years and billions of dollars to develop a new drug. AI techniques such as machine learning, deep learning, natural language processing, reinforcement learning, and generative AI are widely used for drug target identification, biomarker discovery, molecular screening, toxicity prediction, lead optimization, and clinical trial support. Advanced models including Support Vector Machines (SVM), Random Forests (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Transformers improve the prediction of molecular properties and drug-target interactions, while generative AI enables the design of novel therapeutic molecules. This study reviews AI-driven drug discovery methods, presents a structured AI pipeline from data collection to candidate selection, and evaluates performance using metrics such as prediction accuracy, screening efficiency, lead optimization success, and toxicity reduction. Despite its advantages, AI faces challenges including limited high-quality datasets, model bias, interpretability, regulatory uncertainty, computational complexity, and integration with conventional laboratory workflows. The findings indicate that AI significantly improves drug discovery efficiency, reduces research costs, and accelerates pharmaceutical innovation. Future advancements will rely on explainable AI, multimodal biological data integration, federated learning, and stronger regulatory frameworks.
Artificial intelligence (AI) has transformed drug discovery and development by streamlining research processes, reducing development time and costs, and improving the efficiency and success of identifying promising therapeutic candidates. Drug targets can be quickly identified, compound efficacy may be predicted, and drug design can be optimized thanks toartificial intelligence (AI), which assesses massive datasets usingNatural language processing (NLP), machine learning (ML), and deep learning (DL).Through better patient recruitment and data analysis, it refines clinical trial designs and speeds uplead detection using toxicity, potential adverse effects, and pharmacokinetic predictions.The many advantages of AI in drug development are highlighted in this pa per, including increased accuracy, reduced risks, and increased efficiency. Important issues including data quality, model interpretability, and regulatory obstacles are also covered. Improving data will be necessary for future developments in AI - powered d rug discovery.standardization, encouraging openness in the creation of AI models, and bolstering cooperation between pharmaceutical specialists and AI researchers. By tackling these issues, AI has the power to completely transform healthcare by giving pati ents safer, more efficient, and more reasonably priced medications.
{"name":"Shaik Sana","email":"shaiksana79313@gmail.com","affiliat, {"name":"Sarvepalli Revathi","email":"shaiksana79313@gmail.com","affil, {"name":"Yerikala Ramesh","email":"shaiksana79313@gmail.com","affili et al.· Future Journal of Pharmaceut...· 0 citations
This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.
K. Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al.· Future Medicinal Chemistry· 0 citations
It is essential to view AI technologies as an instrumental but supplementary part of decision-making in pharmaceutical research and development rather than a fully independent alternative to traditional hit- and lead-discovery strategies.
Saikat Biswas, Somenath Bhattacharya, Soumallya Chakraborty· International Journal for Re...· 0 citations
Drug repurposing has emerged as a promising strategy to accelerate drug discovery by identifying new therapeutic indications for existing approved or investigational drugs, thereby reducing development time, cost, and clinical risk compared with traditional de novo drug development. The rapid expansion of biomedical big data, together with advances in artificial intelligence (AI) and machine learning (ML), has transformed computational drug repurposing into a data-driven and highly efficient discipline. Conventional machine learning algorithms, including Support Vector Machines, Random Forests, and gradient boosting methods, have demonstrated significant utility in predicting drug-target and drug-disease associations. More recently, deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Transformer-based models, and Graph Neural Networks (GNNs) have enabled the integration of heterogeneous datasets, including chemical structures, transcriptomics, proteomics, metabolomics, pharmacogenomics, protein-protein interaction networks, and electronic health records, substantially improving prediction accuracy.
This review provides a comprehensive overview of AI-driven drug repurposing, covering computational strategies, publicly available biomedical databases, feature representation methods, machine learning and deep learning algorithms, and their applications in cancer, infectious diseases, neurological disorders, cardiovascular diseases, and rare diseases. Furthermore, recent advances in knowledge graphs, explainable artificial intelligence (XAI), federated learning, foundation models, and large language models (LLMs) are discussed as emerging technologies capable of improving prediction reliability, interpretability, and clinical applicability. Current challenges, including data heterogeneity, limited external validation, algorithmic bias, model interpretability, and regulatory barriers, are critically evaluated. Finally, future perspectives focusing on multimodal multi-omics integration, digital twins, real-world evidence, and precision medicine are presented.
S. N, Rachana Sn, Aruna Mv et al.· International Journal For Mu...· 0 citations
Recent literature on the application of artificial intelligence (AI) and data science within bioinformatics-driven cancer drug discovery is synthesized, examining how these tools are reshaping target identification, molecular design, biomarker discovery, and treatment personalization.
Yejide Eniola Dabiri· Magna Scientia Advanced Rese...· 0 citations
Artificial intelligence is revolutionizing drug discovery by accelerating target identification, molecular design, virtual screening, and toxicity prediction, while tackling longstanding challenges like high costs and lengthy timelines in traditional pipelines. This review explores recent AI innovations—such as AlphaFold for protein structure prediction, generative models for de novo drug design, and graph neural networks for drug repurposing—alongside real-world case studies from companies like Exscientia, Insilico Medicine, and BenevolentAI, which have produced clinical candidates like DSP-1181 and rentosertib. Despite these advances, key hurdles persist, including data quality issues, model interpretability, synthetic feasibility for complex molecules, and integration with experimental workflows, underscoring the need for explainable AI, better datasets, and ethical frameworks to bridge research gaps. Looking ahead, hybrid AI-experimental approaches and collaborations between pharma giants and AI startups promise to deliver safer, more personalized therapies faster.
P. Jadhav, R. Pingale, Kanchan Gajanan Gawai et al.· International journal for ad...· 0 citations