Aug 2026· Future Medicinal Chemistry· pp.
1-22
· 0 citations· 57 references
Medicine
TL;DR
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.
Abstract
Cancer drug discovery is a complex process that requires identifying compounds that selectively target malignant cells. While high-throughput screening (HTS) is essential for testing large libraries, it generates vast datasets that are difficult to interpret. Recently, the integration of artificial intelligence (AI), particularly deep learning (DL), has significantly accelerated drug candidate selection. 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. These methods streamline preclinical research by enabling rapid multi-omics analysis and prediction of drug-target interactions. However, challenges regarding data quality, model interpretability, and ethics persist. Emerging paradigms like Explainable AI and federated learning aim to enhance transparency and collaboration while safeguarding privacy. Ultimately, overcoming these barriers through AI-HTS integration holds transformative potential to reduce development costs and improve clinical outcomes for cancer patients.
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.
Joseph Robin· International Journal of Mod...· 0 citations
The integration of Artificial Intelligence (AI) into pharmaceutical research has accelerated drug discovery by streamlining target identification, compound screening, and candidate optimization. Yet, developing effective AI-driven systems remains challenging due to high- dimensional data and the need for expert-driven model tuning. Automated Machine Learning (AutoML) mitigates these issues by automating key stages of the ML pipeline data preprocessing, model selection, hyperparameter tuning, and evaluation thereby enhancing scalability and reducing dependence on domain expertise. AutoML's ability to handle diverse datasets, from omics to lipid nanoparticle (LNP) optimization enabling faster translation of candidates into clinically viable therapeutics. Techniques such as transfer learning, graph neural networks, and transformer-based models enrich molecular representations and improve predictive performance. Advances like DNN-VS further bolster tasks such as virtual screening and bioactivity prediction. Nonetheless, challenges persist in achieving model interpretability and integrating multimodal data. Future efforts should focus on adaptive, domain-aware AutoML strategies to overcome these limitations.
S. Aydın· ITU Journal of Metallurgy an...· 0 citations
The drug discovery process is a long and complicated one, involving multiple steps and issues, starting from target
identification and ending with clinical development. The amount of chemical, biological, and clinical data produced by modern
pharmaceutical companies is tremendous, and there is a continuous need to develop new approaches that could allow for
efficient data mining and identification of relevant information. In this regard, artificial intelligence (AI), and specifically
machine learning (ML) and deep learning (DL) seem to be an attractive choice for researchers for tackling the challenging task
of data analysis.
In silico artificial intelligence has been applied at all stages of the drug discovery and development pipeline, ranging from
molecular target identification, virtual screening, molecular docking, quantitative structure-activity relationship, ADMET
prediction, de novo design, ligand and lead optimisation, computer-assisted synthesis design, drug repurposing, and clinical
trials.
These approaches can facilitate the exploration of large data sets and the prioritisation of compounds for experimental
assessment, thereby reducing the time and cost associated with traditional drug discovery. and expensive traditional drug
discovery.
However, despite the reported benefits and opportunities, there are still some limitations associated with AI-driven drug
discovery, including dependence on large data sets with diverse characteristics, insufficient data for model training, data bias,
overfitting, lack of interpretability, absence of independent evaluation, and regulatory issues.
Therefore, 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 The manuscript is a Review article that focuses on the main areas of application of artificial intelligence for drug
discovery and development, including benefits, drawbacks, ethical issues, and opportunities.
Saikat Biswas, Somenath Bhattacharya, Soumallya Chakraborty· International Journal for Re...· 0 citations
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.
Yue Peng· International Journal of Bio...· 0 citations
Artificial intelligence (AI) has rapidly evolved from a computational research tool into a major driver of innovation across the pharmaceutical development pipeline. Advances in deep learning, foundation models, protein structure prediction, and generative molecular design have accelerated target identification, compound optimization, toxicity prediction, and biomarker discovery. These developments have substantially reduced the time required to generate and prioritize therapeutic hypotheses.
Despite this remarkable progress, the translation of computational predictions into clinically effective medicines remains challenging. Drug development continues to be limited by biological complexity, patient heterogeneity, incomplete datasets, and the need for rigorous experimental and clinical validation. AI can improve decision-making, but it cannot replace the biological evidence required for regulatory approval or patient care.
This editorial discusses the evolving role of AI in modern drug discovery while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration. Rather than viewing AI as a replacement for scientists, clinicians, or pharmacologists, it should be considered a powerful partner that enhances scientific reasoning and accelerates translational research.
The future of pharmaceutical innovation will depend on integrating computational intelligence with experimental pharmacology, clinical medicine, and regulatory science. Responsible implementation—not computational sophistication alone—will determine whether AI ultimately delivers safer, more effective, and more personalized therapies for patients.
Mohsen Zabihi· Advances in Pharmacology and...· 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