Jul 2026· International Journal of Biology and Life Sciences· 0 citations· 15 references
TL;DR
How machine learning, deep learning, natural language processing, and related computational methods are being applied across the drug discovery process is reviewed, with particular attention to AlphaFold-based protein structure prediction, AI-supported virtual screening, generative chemistry, retrosynthetic planning, digital pathology, and the use of real-world clinical data.
Abstract
Traditional pharmaceutical R&D is constrained by substantial financial investment, lengthy development cycles, and a high probability of failure. Artificial intelligence (AI) is now being incorporated into multiple stages of the pharmaceutical pipeline, including target identification, molecular design, synthesis planning, and clinical research. This paper reviews how machine learning, deep learning, natural language processing, and related computational methods are being applied across the drug discovery process. Particular attention is given to AlphaFold-based protein structure prediction, AI-supported virtual screening, generative chemistry, retrosynthetic planning, digital pathology, and the use of real-world clinical data. The review also considers limitations that are often hidden by strong computational performance, such as incomplete training data, limited interpretability, weak interoperability, uncertain external validity, and the continuing need for laboratory and clinical confirmation. In addition, several practical examples from industry and academic research are discussed to connect technical principles with their actual use in pharmaceutical development. Future progress is likely to depend on multimodal data integration, explainable models, robotic design-make-test-analyze cycles, privacy-preserving collaboration, and regulatory frameworks that evaluate both model performance and the quality of the evidence generated. AI should therefore be understood as an augmentation technology: it can prioritize hypotheses and accelerate iteration, but it cannot replace biological reasoning, experimental judgment, or clinical responsibility.
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
The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.
Mohsen Zabihi· Advances in Pharmacology and...· 0 citations
Drug discovery is frequently limited by high attrition rates, and poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles are a major cause of late-stage failure. Therefore, precise ADMET property prediction is necessary to develop safe and effective drug candidates. Traditional experimental assays and rule-based computational procedures are limited by their poor predictive power, cost, and time, despite providing valuable insights. Innovative strategies to deal with these issues have been introduced by developments in artificial intelligence (AI), such as machine learning (ML), deep learning (DL), graph neural networks (GNNs), generative models, and multi-task learning (MTL). AI techniques can better generalize scaffolds, capture interdependencies between pharmacokinetic and toxicological endpoints, and model complex nonlinear relationships by leveraging large, diverse datasets. Explainable AI (XAI) enhances transparency by detecting biological and structural characteristics that are relevant to predictions, even if integrated pipelines combine predictive modeling with molecular creation and optimization. AI-driven ADMET prediction is becoming a vital tool in lowering attrition, speeding up candidate prioritization, and influencing the direction of rational drug development, despite persistent issues with data quality, regulatory acceptance, and synthetic viability.
Satyam Kumar Vishwash, Ram Babu Soni, Ratima Sood et al.· Current Computer - Aided Dru...· 0 citations
This review examines how machine learning, deep learning, natural language processing (NLP), and generative modeling are being applied across medicinal chemistry and drug development, and highlights how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design.
Kaicheng U, Sophia Meixuan Zhang, Ziyu Yu et al.· Chemical Society Reviews· 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
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.
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