https://jddt.in/index.php/jddt/issue/feed 2026-09-10T04:38:00+00:00 JDDT-PUBLISHER editor@jddt.in Open Journal Systems <p><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><span style="text-align: justify;"><strong>(Scientific Journal Impact Factor Value for 2021)</strong></span></span></span></p> <p><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><span style="text-align: justify;"><strong>SJIF 2021 = 6.104 </strong></span></span></span></p> <p><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><span style="text-align: justify;"><strong>Journal of Drug Discovery and Therapeutics (JDDT)</strong> is an international, peer-reviewed, open access, online journal dedicated to the rapid publication of full-length original research papers, short communications, invited reviews, Case studies and editorial commentary and news, Opinions &amp; Perspectives and Book Reviews written at the invitation of the Editor in all areas of the Biomedical and Pharmaceutical Sciences.</span></span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>Medical || Dentistry || Biomedical Sciences || Ayurveda || Homeopathy || </strong></span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;">Anatomy, Physiology, Biochemistry, Molecular Biology, Cell biology, Genetics, Hematology, Pathology, Immunology, Microbiology, Virology, Parasitology, Surgery, Dental Sciences, Sports Physiology, Histopathology, Toxicology and all major disciplines of Biomedical Sciences.<br /><strong>Pharmaceutical Sciences || Allied Sciences </strong></span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;">Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy and Phytochemistry, Pharmacology and Toxicology, Pharmaceutical and Biomedical Analysis, Clinical Research, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology and all major disciplines of Pharmaceutical Sciences.</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;">Articles are published as they are accepted and are freely available on the journal’s website to facilitate rapid and broad dissemination of research findings to a global audience.</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>Top Reasons for publication with us</strong></span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>Quick Quality Review:</strong> The journal has strong international team of editors and reviewers, Rapid Decision and Publication</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>Very Low Publication Fees:</strong> Comparable journals charge a huge sum for each accepted manuscript. JDDT only charge the fees necessary to recoup cost associated with running the journal</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>Other features:</strong> DIDS Assigned and Implemented the Open Review System (ORS).</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>Important Notice:</strong></span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;">Author can now directly send their manuscript as an email attachment to</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;">Innovative Library</span></span></p> <p style="text-align: justify;"><span style="font-family: lucida sans unicode,lucida grande,sans-serif;"><span style="font-size: 14px;"><strong>editor@jddt.in</strong>, <strong>editorjddt.in@gmail.com</strong></span></span></p> <p> </p> https://jddt.in/index.php/jddt/article/view/836 2026-09-10T04:38:00+00:00 Digvijay Singh Rathore editor@jddt.in Hemant Kumar Kuldeep editor@ijmbs.info Pawan Kumar Basniwal editor@jddt.in

<p>Artificial intelligence (AI) is increasingly being investigated as a component of smart pharmaceutical manufacturing through machine learning (ML), artificial neural networks (ANNs), deep learning, computer vision, process analytical technology (PAT), predictive maintenance, and digital twins. The underlying review report identified applications across API synthesis, crystallization, blending, granulation, drying, tableting, coating, quality control, inspection, packaging, continuous manufacturing, scheduling, and supply-chain planning. A central limitation of the current literature is that model performance is often emphasized more than manufacturing readiness. High predictive accuracy alone does not establish suitability for a GMP environment because data quality, representativeness, robustness, interoperability, validation, explainability, cybersecurity, human oversight, and lifecycle management also determine practical readiness. This review therefore evaluates AI using an evidence-maturity perspective distinguishing conceptual research, laboratory proof-of-concept, pilot/production-relevant evidence, and routine industrial implementation. It synthesizes implementation barriers and proposes an integrated adoption pathway linking manufacturing-problem definition, data readiness, AI selection, model development, risk-based GMP assessment, pilot testing, industrial integration, and continuous monitoring. The review concludes that the next phase of pharmaceutical AI should focus less on isolated high-performing models and more on validated, trustworthy, integrated, and lifecycle-managed systems that improve quality, productivity, reliability, and patient safety.</p> <p><strong>Keywords:</strong> Artificial intelligence; machine learning; pharmaceutical manufacturing; process analytical technology; digital twins; predictive maintenance; Pharma 4.0; GMP; model validation; manufacturing readines</p>

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