Deep Learning in Drug Design

Deep Learning in Drug Design by Qifeng Bai, Tingyang Xu, and Junzhou Huang is a definitive, practical guide for anyone ready to harness artificial intelligence to accelerate drug discovery. This timely volume captures the surge of deep learning applications across medicinal chemistry, computational biology, and pharmaceutical R&D, translating complex algorithms into actionable strategies.

Inside, readers will find clear explanations of core techniques—graph neural networks, generative models, molecular representation learning, and ADMET prediction—framed around realistic drug-design challenges. The authors combine theoretical clarity with practical insight, showing how predictive modeling, virtual screening, and de novo molecule generation can be integrated into discovery pipelines. Case studies and evaluation best practices emphasize reproducibility and decision-making that matters to chemists and data scientists alike.

Ideal for graduate students, computational chemists, biotech innovators, and pharma teams across North America, Europe, and Asia, this book bridges the gap between academic research and industrial application. Whether you are developing lead compounds, optimizing candidate properties, or building AI-enabled workflows, you’ll gain frameworks and perspectives that cut development time and improve hit quality.

Engagingly written and technically rich, Deep Learning in Drug Design empowers you to adopt state-of-the-art AI tools with confidence. Add this essential reference to your library and lead the next wave of discovery in biotech and drug development worldwide. Purchase today to transform data into therapeutics.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.