{"product_id":"generative-ai-for-molecular-drug-design-with-python-diffusion-models-vaes-gans-and-transformers-for-computational-chemistry-9798249319229","title":"Generative AI for Molecular Drug Design with Python: Diffusion Models, VAEs, GANs, and Transformers for Computational Chemistry","description":"\u003cp\u003e • Author(s): Danny Munrow | Livia Arden\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming Languages - Python\u003c\/p\u003e\u003cp\u003e\u003cb\u003eReactive Publishing\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eArtificial intelligence is reshaping pharmaceutical research by enabling the computational generation of novel molecular structures. \u003ci\u003eGenerative AI for Molecular Drug Design with Python\u003c\/i\u003e provides a technical, implementation-focused guide to building and evaluating generative models for small-molecule discovery.\u003c\/p\u003e\u003cp\u003eThis book bridges machine learning engineering and computational chemistry. It explores how modern generative architectures can be applied to molecular representation, property prediction, and candidate generation using Python-based tooling.\u003c\/p\u003e\u003cp\u003eTopics include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003eMolecular representations: SMILES, graphs, embeddings, and chemical descriptors\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eVariational Autoencoders (VAEs) for latent space exploration\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eGenerative Adversarial Networks (GANs) for molecular synthesis\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDiffusion models for structure generation and refinement\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eTransformer architectures applied to sequence-based chemical modeling\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDataset preparation, validation, and chemical constraint enforcement\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eEvaluating novelty, validity, and synthesizability\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eIntegrating generative models into drug discovery workflows\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePractical examples leverage PyTorch and common cheminformatics libraries to demonstrate end-to-end model development, from dataset preprocessing to molecular sampling and evaluation.\u003c\/p\u003e\u003cp\u003eDesigned for quantitative researchers, ML engineers, computational chemists, and advanced students, this book focuses on implementation depth rather than high-level theory alone. Readers should have prior familiarity with Python and foundational machine learning concepts.\u003c\/p\u003e\u003cp\u003eThe result is a rigorous, systems-level guide to applying generative AI in modern drug design pipelines.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47569126785175,"sku":"9798249319229","price":3315.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798249319229.webp?v=1774873316","url":"https:\/\/atlanticbooks.com\/products\/generative-ai-for-molecular-drug-design-with-python-diffusion-models-vaes-gans-and-transformers-for-computational-chemistry-9798249319229","provider":"Atlantic Books","version":"1.0","type":"link"}