Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

Abstract

Computational methods play a key role in the design of therapeutically important molecules for modern drug development. With these “in silico” approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough in drug discovery. Machine learning (ML) methods are used to predict compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties to evaluate the drugs and their various applications. Modern artificial intelligence (AI) has the capacity to significantly enhance the role of computational methodology in drug discovery. Use of AI in drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials will certainly reduce the human workload as well as achieving targets in a short period of time. This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

“…These technologies analyze biological data to identify disease-associated targets, predict drug interactions, and optimize research processes, leading to more efficient drug discovery and increased chances of successful approvals (Đuriš et al, 2021). Machine learning tools help in analyzing large datasets generated by Process Analytical Technologies, providing a be er understanding of pharmaceutical formulations and processing (Srivastava, 2022). Additionally, AI enhances the prediction of pharmacokinetics, toxicity, and drug properties, facilitating the design of more effective and safer therapeutics.…”

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

“…These technologies analyze biological data to identify disease-associated targets, predict drug interactions, and optimize research processes, leading to more efficient drug discovery and increased chances of successful approvals (Đuriš et al, 2021). Machine learning tools help in analyzing large datasets generated by Process Analytical Technologies, providing a better understanding of pharmaceutical formulations and processing (Srivastava, 2022). Additionally, AI enhances the prediction of pharmacokinetics, toxicity, and drug properties, facilitating the design of more effective and safer therapeutics.…”

Perkembangan Penerapan Kecerdasan Artifisial di Bidang Kesehatan dan Peran Regulasi Kotak Pasir (Regulatory Sandbox) dalam Memodulasi Prosesnya

“…Tentu ada pula Gambar 1. Diagram Metode Utama Kecerdasan Artifisial [1] Gambar 2. Peta Teritorial Kecerdasan Artifisial [2] konsep deep learning dan cognitive artificial intelligence yang salah satu modelnya adalah knowledge growing system (KGS) karya peneliti Ahmad (Prof. Dr. Ir.…”