Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
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.
Abstract
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.
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.
Yue Peng· International Journal of Bio...· 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.
{"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
Drug discovery is a time-consuming and resource-intensive process with a development period of more than ten years and a clinical attrition rate of more than 90%. Despite its contributions to rational drug design, computer-aided drug design has been constrained by limited scalability, overreliance on molecular descriptors, and incomplete modeling of complex biological systems. The emergence of artificial intelligence (AI) has transformed this landscape. AI-based drug discovery platforms have shifted the paradigm from a narrow focus to a comprehensive platform that covers target identification using graph-based drug-target interaction models. Additionally, deep-learning-based docking techniques, such as GNINA and AtomNet, de novo design approaches, such as REINVENT and RANC, and multi-task ADMET predictors, such as ADMETlab 2.0, are all parts of the AI-based drug discovery platform. In addition, AlphaFold has recently predicted more than 200 million protein structures, substantially expanding the pool of accessible drug targets. This review focuses on the AI-based approaches for target discovery, virtual screening, molecular generation, lead optimization, retro-synthesis, and structural modeling while also addressing issues of dataset bias, reproducibility, and real-world applicability. In this review, we discuss the emerging trends of AI-based drug discovery computational tools, which might change the face of medicinal chemistry. This review provides a balanced overview of AI-based drug discovery, including the limitations and challenges, to provide a framework to move this emerging field of science to a more robust, reproducible and clinically applicable platform.
Ryena Dhir, Pitam Ghosh, D. Sharma et al.· RSC Advances· 0 citations
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.
Joseph Robin· International Journal of Mod...· 0 citations
Drug discovery is a tedious process that takes a long time and incurs high costs in developing an approved drug for clinical use. The long time and high expenditure are due to various phases of drug discovery and do not guarantee the success of the drug for clinical use, and about 90% of potential drug candidates suffer failure in phase-I clinical trials. The drug molecule qualifying for phase I clinical trial after passing through the preclinical stages is a significant milestone for both research institutes and pharmaceutical companies. Therefore, there is a need to explore alternatives for the drug discovery process. In this regard, Artificial Intelligence may provide significant assistance in different processes of drug discovery. This review discusses the applications of AI in diverse and prominent processes of drug discovery, like identification of a diseased state, identification of target, development of lead compounds, virtual screening, and drug toxicity. AI has proved its potential in the cheaper, easier, and timely development of drugs for different ailments. The application of AI not only enhances the quality of the drug development process but also assures better safety in the treatment and diagnosis of disease. AI introduces automation in drug development and clinical trials, minimizing the chances of human error. Focusing on the quality and quantity of the data, ethical consideration of the patient data may help in revolutionizing the process of synthetic drug development and treatment.
N. Srivastava, Abhishek Srivastava, D. Chaturvedi et al.· Current pharmaceutical desig...· 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