title: Automatically updating predictive modeling workflows support decision-making in drug design.
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.
authors: Department of Small Molecule Discovery Research, Boehringer Ingelheim Pharmaceuticals, 900 Ridgebury Road, Ridgefield, CT 06877-0368, USA.
authors: Ingo
authors: I
authors: Muegge
authors: Department of Small Molecule Discovery Research, Boehringer Ingelheim Pharmaceuticals, 900 Ridgebury Road, Ridgefield, CT 06877-0368, USA.
authors: Jörg
authors: J
authors: Bentzien
authors: Department of Small Molecule Discovery Research, Boehringer Ingelheim Pharmaceuticals, 900 Ridgebury Road, Ridgefield, CT 06877-0368, USA.
authors: Prasenjit
authors: P
authors: Mukherjee
authors: Department of Small Molecule Discovery Research, Boehringer Ingelheim Pharmaceuticals, 900 Ridgebury Road, Ridgefield, CT 06877-0368, USA.
authors: Robert O
authors: RO
authors: Hughes
mesh_terms: D001331:Automation; D003198:Computer Simulation; D003661:Decision Support Techniques; D015195:Drug Design; D057188:Workflow
keywords: QSAR model; computational chemistry; drug design; human dose prediction; molecular docking; qualified data