Reaction-Based Enumeration, Active Learning, and Free
Energy Calculations To Rapidly Explore Synthetically Tractable Chemical
Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors
Posted on 2019-08-22 - 13:35
The hit-to-lead and lead optimization
processes usually involve
the design, synthesis, and profiling of thousands of analogs prior
to clinical candidate nomination. A hit finding campaign may begin
with a virtual screen that explores millions of compounds, if not
more. However, this scale of computational profiling is not frequently
performed in the hit-to-lead or lead optimization phases of drug discovery.
This is likely due to the lack of appropriate computational tools
to generate synthetically tractable lead-like compounds in silico,
and a lack of computational methods to accurately profile compounds
prospectively on a large scale. Recent advances in computational power
and methods provide the ability to profile much larger libraries of
ligands than previously possible. Herein, we report a new computational
technique, referred to as “PathFinder”, that uses retrosynthetic
analysis followed by combinatorial synthesis to generate novel compounds
in synthetically accessible chemical space. In this work, the integration
of PathFinder-driven compound generation, cloud-based FEP simulations,
and active learning are used to rapidly optimize R-groups, and generate
new cores for inhibitors of cyclin-dependent kinase 2 (CDK2). Using
this approach, we explored >300 000 ideas, performed >5000
FEP simulations, and identified >100 ligands with a predicted IC50 < 100 nM, including four unique cores. To our knowledge,
this is the largest set of FEP calculations disclosed in the literature
to date. The rapid turnaround time, and scale of chemical exploration,
suggests that this is a useful approach to accelerate the discovery
of novel chemical matter in drug discovery campaigns.
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Konze, Kyle D.; Bos, Pieter H.; Dahlgren, Markus K.; Leswing, Karl; Tubert-Brohman, Ivan; Bortolato, Andrea; et al. (2019). Reaction-Based Enumeration, Active Learning, and Free
Energy Calculations To Rapidly Explore Synthetically Tractable Chemical
Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors. ACS Publications. Collection. https://doi.org/10.1021/acs.jcim.9b00367
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AUTHORS (9)
KK
Kyle D. Konze
PB
Pieter H. Bos
MD
Markus K. Dahlgren
KL
Karl Leswing
IT
Ivan Tubert-Brohman
AB
Andrea Bortolato
BR
Braxton Robbason
RA
Robert Abel
SB
Sathesh Bhat
KEYWORDS
chemical explorationActive Learninglacknovel chemical mattercloud-based FEP simulationsFree Energy Calculationsliganddrug discoveryhit-to-leadretrosynthetic analysisturnaround timecombinatorial synthesischemical spaceapproachmethodcorePathFinder-driven compound generationnovel compoundsprofile compoundsOptimize Potencysynthetically tractable lead-like compoundsRecent advancescandidate nominationdrug discovery campaignsCyclin-Dependent Kinase 2 InhibitorsCDKoptimization processesIC 50Reaction-Based Enumerationcyclin-dependent kinase 2optimization phases100 nMRapidly Explore Synthetically Tractable Chemical SpaceFEP calculations