Multi-Scale Approaches in Drug Discovery. From Empirical Knowledge to In Silico Experiments and Back

Content: 
Front Matter,Copyright,ContributorsEntitled to full textChapter 1 - Profiling Drug Binding by Thermodynamics: Key to Understanding, Pages 1-36, G. Klebe
Chapter 2 - Machine Learning Approach to Predict Enzyme Subclasses, Pages 37-53, R. Concu, H. González-Díaz, M.N.D.S. Cordeiro
Chapter 3 - Multitasking Model for Computer-Aided Design and Virtual Screening of Compounds With High Anti-HIV Activity and Desirable ADMET Properties, Pages 55-81, V.V. Kleandrova, A. Speck-Planche
Chapter 4 - Alkaloids From the Family Menispermaceae: A New Source of Compounds Selective for β-Adrenergic Receptors, Pages 83-98, M.F. Alves, M.T. Scotti, L. Scotti, S. Golzio dos Santos, M. de Fátima Formiga Melo Diniz
Chapter 5 - Natural Chemotherapeutic Agents for Cancer, Pages 99-126, R. Dutt, V. Garg, A.K. Madan
Chapter 6 - Speeding Up the Virtual Design and Screening of Therapeutic Peptides: Simultaneous Prediction of Anticancer Activity and Cytotoxicity, Pages 127-147, A. Speck-Planche, M.N.D.S. Cordeiro
Chapter 7 - Flavonoids From Asteraceae as Multitarget Source of Compounds Against Protozoal Diseases, Pages 149-190, É.B.V.S. Cavalcanti, V. de Paulo Emerenciano, L. Scotti, M.T. Scotti
Chapter 8 - Quasi-SMILES as a Novel Tool for Prediction of Nanomaterials′ Endpoints, Pages 191-221, A.P. Toropova, A.A. Toropov, A.M. Veselinović, J.B. Veselinović, D. Leszczynska, J. Leszczynski
Index, Pages 223-227