Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
نویسندگان
چکیده
منابع مشابه
Advances in multiparameter optimization methods for de novo drug design.
INTRODUCTION A high-quality drug must achieve a balance of physicochemical and absorption, distribution, metabolism and elimination properties, safety and potency against its therapeutic target(s). Multiparameter optimization (MPO) methods guide the simultaneous optimization of multiple factors to quickly target compounds with the highest chance of downstream success. MPO can be combined with '...
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A common step in drug design is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure of a compound relates to its biological activity. There is growing interest in the application of machine learning techniques in QSAR modeling research. However, no single technique can claim to be uniformly supe...
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Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. They are inherently suitable for use with 'noisy', high dimensional (many variables) data, as is commonly used in cheminformatic (i.e. In silico screening), bioinformatic (i.e. bio-marker studies, ...
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Drug discovery and development is a complex, lengthy process, and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy, or toxicity. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks...
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ژورنال
عنوان ژورنال: International Journal of Molecular Sciences
سال: 2021
ISSN: 1422-0067
DOI: 10.3390/ijms22041676