2 Machine learning transformations in drug discovery: a paradigm shift in development strategies
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Siddhartha Roy
Abstract
The pharmaceutical industry is undergoing a massive transformative revolution by the integration of machine learning (ML) into drug discovery and development processes. This paper provides a comprehensive overview of the paradigm shift brought about by ML techniques, including predictive modeling, virtual screening, and de novo drug design. The applications of ML in target identification, compound screening, and lead optimization reveal its potential to significantly accelerate the drug development pipeline. Innovative ML-driven approaches are reshaping preclinical and clinical trials, leading to more efficient trial designs, patient stratification, and treatment response prediction. Moreover, advancements in explicable AI are addressing the interpretability concerns associated with complex ML models and building trust among researchers and clinicians.
The paper investigates successful case studies, including Atomwise’s Ebola drug discovery and DeepMind’s AlphaFold for protein folding, highlighting the tangible impact of ML on identifying new drug candidates. However, challenges such as limited data availability, interpretability, ethical considerations, and integration with traditional processes require collaborative efforts and strategic solutions to realize the full potential of ML. ML promises accelerated drug development timelines, precision medicine personalized to individual patient profiles, cost reduction through resource optimization, and the emergence of a collaborative ecosystem involving pharmaceutical companies, research institutions, and regulatory bodies. As ethical guidelines and regulatory frameworks evolve, the role of ML in drug discovery will be guided to revolutionize healthcare by addressing current challenges and advancing the frontiers of medical science.
Abstract
The pharmaceutical industry is undergoing a massive transformative revolution by the integration of machine learning (ML) into drug discovery and development processes. This paper provides a comprehensive overview of the paradigm shift brought about by ML techniques, including predictive modeling, virtual screening, and de novo drug design. The applications of ML in target identification, compound screening, and lead optimization reveal its potential to significantly accelerate the drug development pipeline. Innovative ML-driven approaches are reshaping preclinical and clinical trials, leading to more efficient trial designs, patient stratification, and treatment response prediction. Moreover, advancements in explicable AI are addressing the interpretability concerns associated with complex ML models and building trust among researchers and clinicians.
The paper investigates successful case studies, including Atomwise’s Ebola drug discovery and DeepMind’s AlphaFold for protein folding, highlighting the tangible impact of ML on identifying new drug candidates. However, challenges such as limited data availability, interpretability, ethical considerations, and integration with traditional processes require collaborative efforts and strategic solutions to realize the full potential of ML. ML promises accelerated drug development timelines, precision medicine personalized to individual patient profiles, cost reduction through resource optimization, and the emergence of a collaborative ecosystem involving pharmaceutical companies, research institutions, and regulatory bodies. As ethical guidelines and regulatory frameworks evolve, the role of ML in drug discovery will be guided to revolutionize healthcare by addressing current challenges and advancing the frontiers of medical science.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
