The Role of Artificial Intelligence and Machine Learning in Drug Discovery and Development
Bandaru Revanth
Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.
Syed Shuja Asrar
Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.
Binaya Sapkota
Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.
Karnati Vandana
Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.
Kanala Somasekhar Reddy
Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.
Bhupalam Pradeep Kumar *
Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.
*Author to whom correspondence should be addressed.
Abstract
The symbiotic integration of artificial intelligence (AI) and pharmacology marks a paradigm shift in medicine discovery and development. Traditional approaches, formerly constrained by the complications of target identification, high-outturn webbing, and clinical trials, are yielding to the transformative power of AI. This review navigates the elaboration of technology in medicine discovery, from literal limitations to the emergence of AI as a catalyst for effectiveness and perfection. AI's operations in target identification and high-outturn webbing accelerate processes, furnishing unknown perceptivity to implicit medicine campaigners. In preclinical and clinical development, prophetic modeling for toxin assessment and case position in clinical trials are reshaping the geography, offering a more ethical and individualized approach. still, this technological advancement isn't without challenges. Data quality, bias in AI models, interpretability, and nonsupervisory considerations demand careful navigation. Success stories, from AI- AI-designed medicines entering clinical trials to the repurposing of composites, punctuate the palpable impact of this community. Looking ahead, nonstop advancements in AI algorithms and the integration of multi-omics data promise a period of accelerated timelines and substantiated drugs. As we stand at the nexus of invention and responsibility, the unborn geography of medicine discovery and development motions, driven by the pledge of AI to revise healthcare results with effectiveness, perfection, and case- centricity.
Keywords: Pharmacology, drug discovery, drug development, evolution, personalized medicines, computational models ethical considerations, multi-omics data, Innovation patient-centric health care