In:
Future Medicinal Chemistry, Future Science Ltd, Vol. 8, No. 14 ( 2016-09), p. 1779-1796
Abstract:
Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure–activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.
Type of Medium:
Online Resource
ISSN:
1756-8919
,
1756-8927
DOI:
10.4155/fmc-2016-0070
Language:
English
Publisher:
Future Science Ltd
Publication Date:
2016
SSG:
15,3
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