{"product_id":"graph-neural-networks-for-molecular-alice-schwartz-9798184214115","title":"Graph Neural Networks for Molecular Discovery with Python: Geometric Deep Learning, Molecule Generation, and Property Prediction","description":"\u003cb\u003eReactive Publishing\u003c\/b\u003e\u003cp\u003eDiscover the future of molecular discovery with the power of Graph Neural Networks and Geometric Deep Learning.\u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eGraph Neural Networks for Molecular Discovery with Python\u003c\/i\u003e, Livia Arden delivers a practical, hands-on guide to applying cutting-edge geometric deep learning techniques to one of the most exciting frontiers in science: accelerating the design and optimization of new molecules. Whether you're working in drug discovery, materials science, or chemical engineering, this book equips you with the tools to model molecular structures as graphs and extract powerful insights that traditional methods simply cannot match.\u003c\/p\u003eWhat You'll Learn\u003cul\u003e\n\u003cli\u003eMaster the fundamentals of \u003cb\u003eGraph Neural Networks (GNNs)\u003c\/b\u003e and how they naturally represent atoms, bonds, and molecular geometry.\u003c\/li\u003e\n\u003cli\u003eBuild and train sophisticated models for \u003cb\u003eproperty prediction\u003c\/b\u003e - from solubility and toxicity to bioactivity and quantum mechanical properties.\u003c\/li\u003e\n\u003cli\u003eExplore \u003cb\u003emolecule generation\u003c\/b\u003e techniques, including variational autoencoders, generative adversarial networks, and diffusion models adapted for graphs.\u003c\/li\u003e\n\u003cli\u003eImplement real-world workflows using Python libraries such as PyTorch Geometric, DGL, RDKit, and NetworkX.\u003c\/li\u003e\n\u003cli\u003eTackle challenges like molecular featurization, graph pooling, attention mechanisms, and scalable training on large chemical datasets.\u003c\/li\u003e\n\u003cli\u003eApply advanced topics including equivariant networks, 3D molecular modeling, and multi-task learning for accelerated virtual screening.\u003c\/li\u003e\n\u003c\/ul\u003eHands-On and Production-Ready\u003cp\u003eEvery concept is reinforced with clean, well-documented Python code examples that you can immediately adapt to your own research or projects. From loading SMILES strings and building molecular graphs to deploying predictive models and generating novel candidate compounds, this book bridges theory and practice with a strong emphasis on reproducibility and real-world impact.\u003c\/p\u003eWho This Book Is For\u003cul\u003e\n\u003cli\u003eData scientists and machine learning engineers eager to apply GNNs to scientific domains.\u003c\/li\u003e\n\u003cli\u003eComputational chemists and researchers looking to modernize their discovery pipelines.\u003c\/li\u003e\n\u003cli\u003eGraduate students and professionals in cheminformatics, bioinformatics, and materials informatics.\u003c\/li\u003e\n\u003cli\u003eAnyone interested in the intersection of geometric deep learning and molecular science.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Alice Schwartz,Livia Arden\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798184214115\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/24\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 538\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.41lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 1.33d","brand":"Alice Schwartz","offers":[{"title":"Paperback","offer_id":49097380593919,"sku":"9798184214115","price":39.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/graph-neural-networks-for-molecular-alice-schwartz-9798184214115","provider":"WR Book House","version":"1.0","type":"link"}