In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Lauro Ribeirode Souza Neto; José TeófiloMoreira-Filho; Bruno JuniorNeves; Rocío Lucía Beatriz RiverosMaidana; Ana Carolina RamosGuimarães; Nicholas Furnham ORCID logo; Carolina HortaAndrade; Floriano PaesSilva; (2020) In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery. Frontiers in chemistry, 8. 93-. ISSN 2296-2646 DOI: 10.3389/fchem.2020.00093
Copy

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.



picture_as_pdf
In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.pdf
subject
Published Version
Available under Creative Commons: 3.0

View Download

Explore Further

Read more research from the creator(s):

Find work associated with the faculties and division(s):

Find work associated with the research centre(s):

Find work from this publication: