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A guide to improving efficiency in drug discovery by analyzing and approaching the development of new treatments from many different angles

Table of Contents

1. Profiling Drug Binding by Thermodynamics: Key to Understanding 2. Machine Learning Approach to Predict Enzyme Subclasses 3. Multitasking Model for Computer-Aided Design and Virtual Screening of Compounds With High Anti-HIV Activity and Desirable ADMET Properties 4. Alkaloids From the Family Menispermaceae: A New Source of Compounds Selective for ß-Adrenergic Receptors 5. Natural Chemotherapeutic Agents for Cancer 6. Speeding Up the Virtual Design and Screening of Therapeutic Peptides: Simultaneous Prediction of Anticancer Activity and Cytotoxicity 7. Flavonoids From Asteraceae as Multitarget Source of Compounds Against Protozoal Diseases 8. Quasi-SMILES as a Novel Tool for Prediction of Nanomaterials' Endpoints

About the Author

Since graduating from the University of Oriente, Cuba, Alejandro has swiftly been making a name for himself as a leading expert in computational tools for multi-scale drug discovery. In the past 5 years, he has published 54 papers which have been cited in excess of 640 times, giving him a personal field-weighted citation impact of 2.19. He has collaborated with teams from around the globe, and his research interests include chemo-bioinformatics, computer-aided drug design, computational toxicology, QSPR/QSAR/QSTR, peptide discovery, antimicrobial research, anticancer research and neurodegenerative diseases.