In silico strategies to support fragment-to-lead optimization in drug discovery
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Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last
two decades to become a successful key technology in the pharmaceutical industry for
early stage drug discovery and development. The FBDD strategy consists of screening
low molecular weight compounds against macromolecular targets (usually proteins)
of clinical relevance. These small molecular fragments can bind at one or more sites
on the target and act as starting points for the development of lead compounds. In
developing the fragments attractive features that can translate into compounds with
favorable physical, pharmacokinetics and toxicity (ADMET—absorption, distribution,
metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled
fragment screening campaigns use a combination of screening by a range of biophysical
techniques, such as differential scanning fluorimetry, surface plasmon resonance,
and thermophoresis, followed by structural characterization of fragment binding using
NMR or X-ray crystallography. Structural characterization is also used in subsequent
analysis for growing fragments of selected screening hits. The latest iteration of
the FBDD workflow employs a high-throughput methodology of massively parallel
screening by X-ray crystallography of individually soaked fragments. In this review
we will outline the FBDD strategies and explore a variety of in silico approaches to
support the follow-up fragment-to-lead optimization of either: growing, linking, and
merging. These fragment expansion strategies include hot spot analysis, druggability
prediction, SAR (structure-activity relationships) by catalog methods, application of
machine learning/deep learning models for virtual screening and several de novo design
methods for proposing synthesizable new compounds. Finally, we will highlight recent
case studies in fragment-based drug discovery where in silico methods have successfully
contributed to the development of lead compounds.
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SOUZA NETO, Lauro Ribeiro de et al. In silico strategies to support fragment-to-lead optimization in drug discovery. Frontiers in Chemistry, Lausanne, v. 8, p. 93, 2020. DOI: 10.3389/fchem.2020.00093. Disponível em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040036/. Acesso em: 9 set. 2024.