Ed Griffen

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Highly experienced, energetic, creative medicinal chemist. • Delivered design successes against potency, selectivity & pharmacokinetic issues with a particular strength in lead generation. • Expert practitioner of data-driven, hypothesis based design. • Experience of working in very wide range of target classes in Oncology, Infection and CNS/Pain. • Lead chemist on 10 projects over 17 years. • Experienced at international working with Swedish, North-American, Indian and Chinese colleagues. Skilled in: • Ligand-based, structure-based and fragment-based design. • Screening design and work up (full HTS, directed screens & libraries, phenotypic screening). • Innovator in computational medi...

medicinal chemistry mmpa toxicology ai interpretable ai drug discovery big data qsar admet medchemica machine learning pharmacokinetics computational chemistry cheminformatics potency toxophore ibsa sar matched molecular pairs dmpk pharmacophore pharmcophore

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Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair Analysis platform: standardization of SMIRKS enables knowledge exchange

Vor 6 Jahren • 861 Aufrufe

Extracting actionable knowledge from large scale in vitro pharmacology data

Vor 7 Jahren • 221 Aufrufe

Ed Griffen

Emerging Challenges for Artificial Intelligence in Medicinal Chemistry

Explainable AI in Drug Hunting

SCI What can Big Data do for Chemistry 2017 MedChemica

Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017

Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair Analysis platform: standardization of SMIRKS enables knowledge exchange

Extracting actionable knowledge from large scale in vitro pharmacology data