Literature DB >> 32133344

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

Lauro Ribeiro de Souza Neto1, José Teófilo Moreira-Filho2, Bruno Junior Neves2,3, Rocío Lucía Beatriz Riveros Maidana1,4, Ana Carolina Ramos Guimarães4, Nicholas Furnham5, Carolina Horta Andrade2, Floriano Paes Silva1.   

Abstract

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.
Copyright © 2020 de Souza Neto, Moreira-Filho, Neves, Maidana, Guimarães, Furnham, Andrade and Silva.

Entities:  

Keywords:  de novo design; drug discovery; fragment-based; hot spot analysis; in silico methods; lead discovery; machine learning; optimization

Year:  2020        PMID: 32133344      PMCID: PMC7040036          DOI: 10.3389/fchem.2020.00093

Source DB:  PubMed          Journal:  Front Chem        ISSN: 2296-2646            Impact factor:   5.221


  122 in total

1.  Structural evidence for ligand specificity in the binding domain of the human androgen receptor. Implications for pathogenic gene mutations.

Authors:  P M Matias; P Donner; R Coelho; M Thomaz; C Peixoto; S Macedo; N Otto; S Joschko; P Scholz; A Wegg; S Bäsler; M Schäfer; U Egner; M A Carrondo
Journal:  J Biol Chem       Date:  2000-08-25       Impact factor: 5.157

2.  Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery.

Authors:  Hernani Leonardo Silvestre; Thomas L Blundell; Chris Abell; Alessio Ciulli
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-19       Impact factor: 11.205

3.  Identification of novel ROS inducer by merging the fragments of piperlongumine and dicoumarol.

Authors:  Xiaojuan Xu; Xia Fang; Jun Wang; Hong Zhu
Journal:  Bioorg Med Chem Lett       Date:  2016-08-06       Impact factor: 2.823

4.  Identification of novel lysine demethylase 5-selective inhibitors by inhibitor-based fragment merging strategy.

Authors:  Yuka Miyake; Yukihiro Itoh; Atsushi Hatanaka; Yoshinori Suzuma; Miki Suzuki; Hidehiko Kodama; Yoshinobu Arai; Takayoshi Suzuki
Journal:  Bioorg Med Chem       Date:  2019-02-04       Impact factor: 3.641

5.  Cancer therapy. Targeting the poison within.

Authors:  Veronique A J Smits; David A Gillespie
Journal:  Cell Cycle       Date:  2014       Impact factor: 4.534

6.  Prediction of synthetic accessibility based on commercially available compound databases.

Authors:  Yoshifumi Fukunishi; Takashi Kurosawa; Yoshiaki Mikami; Haruki Nakamura
Journal:  J Chem Inf Model       Date:  2014-12-03       Impact factor: 4.956

7.  The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database.

Authors:  Richard J Hall; Christopher W Murray; Marcel L Verdonk
Journal:  J Med Chem       Date:  2017-07-15       Impact factor: 7.446

8.  Structure-Guided Screening for Functionally Selective D2 Dopamine Receptor Ligands from a Virtual Chemical Library.

Authors:  Barbara Männel; Mariama Jaiteh; Alexey Zeifman; Alena Randakova; Dorothee Möller; Harald Hübner; Peter Gmeiner; Jens Carlsson
Journal:  ACS Chem Biol       Date:  2017-09-19       Impact factor: 5.100

9.  Validation and development of MTH1 inhibitors for treatment of cancer.

Authors:  U Warpman Berglund; K Sanjiv; H Gad; C Kalderén; T Koolmeister; T Pham; C Gokturk; R Jafari; G Maddalo; B Seashore-Ludlow; A Chernobrovkin; A Manoilov; I S Pateras; A Rasti; A-S Jemth; I Almlöf; O Loseva; T Visnes; B O Einarsdottir; F Z Gaugaz; A Saleh; B Platzack; O A Wallner; K S A Vallin; M Henriksson; P Wakchaure; S Borhade; P Herr; Y Kallberg; P Baranczewski; E J Homan; E Wiita; V Nagpal; T Meijer; N Schipper; S G Rudd; L Bräutigam; A Lindqvist; A Filppula; T-C Lee; P Artursson; J A Nilsson; V G Gorgoulis; J Lehtiö; R A Zubarev; M Scobie; T Helleday
Journal:  Ann Oncol       Date:  2016-11-08       Impact factor: 32.976

10.  Biophysical screening for the discovery of small-molecule ligands.

Authors:  Alessio Ciulli
Journal:  Methods Mol Biol       Date:  2013
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  27 in total

1.  Application of Sensitivity Analysis to Discover Potential Molecular Drug Targets.

Authors:  Malgorzata Kardynska; Jaroslaw Smieja; Pawel Paszek; Krzysztof Puszynski
Journal:  Int J Mol Sci       Date:  2022-06-13       Impact factor: 6.208

2.  A Deep Generative Model for Molecule Optimization via One Fragment Modification.

Authors:  Ziqi Chen; Martin Renqiang Min; Srinivasan Parthasarathy; Xia Ning
Journal:  Nat Mach Intell       Date:  2021-12-09

3.  Derivatization Design of Synthetically Accessible Space for Optimization: In Silico Synthesis vs Deep Generative Design.

Authors:  Gergely M Makara; László Kovács; István Szabó; Gábor Pőcze
Journal:  ACS Med Chem Lett       Date:  2021-01-07       Impact factor: 4.345

4.  Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations.

Authors:  Maicol Bissaro; Giovanni Bolcato; Matteo Pavan; Davide Bassani; Mattia Sturlese; Stefano Moro
Journal:  ChemMedChem       Date:  2021-05-06       Impact factor: 3.540

5.  Experiences From Developing Software for Large X-Ray Crystallography-Driven Protein-Ligand Studies.

Authors:  Nicholas M Pearce; Rachael Skyner; Tobias Krojer
Journal:  Front Mol Biosci       Date:  2022-04-11

6.  Substrate-Inspired Fragment Merging and Growing Affords Efficacious LasB Inhibitors.

Authors:  Cansu Kaya; Isabell Walter; Samir Yahiaoui; Asfandyar Sikandar; Alaa Alhayek; Jelena Konstantinović; Andreas M Kany; Jörg Haupenthal; Jesko Köhnke; Rolf W Hartmann; Anna K H Hirsch
Journal:  Angew Chem Int Ed Engl       Date:  2021-12-13       Impact factor: 16.823

7.  AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization.

Authors:  Jacob O Spiegel; Jacob D Durrant
Journal:  J Cheminform       Date:  2020-04-17       Impact factor: 5.514

8.  Room-temperature crystallography using a microfluidic protein crystal array device and its application to protein-ligand complex structure analysis.

Authors:  Masatoshi Maeki; Sho Ito; Reo Takeda; Go Ueno; Akihiko Ishida; Hirofumi Tani; Masaki Yamamoto; Manabu Tokeshi
Journal:  Chem Sci       Date:  2020-08-25       Impact factor: 9.825

Review 9.  Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Authors:  José T Moreira-Filho; Arthur C Silva; Rafael F Dantas; Barbara F Gomes; Lauro R Souza Neto; Jose Brandao-Neto; Raymond J Owens; Nicholas Furnham; Bruno J Neves; Floriano P Silva-Junior; Carolina H Andrade
Journal:  Front Immunol       Date:  2021-05-31       Impact factor: 7.561

10.  Exploring the inhibitory potentials of Momordica charantia bioactive compounds against Keap1-Kelch protein using computational approaches.

Authors:  Temitope Isaac Adelusi; Misbaudeen Abdul-Hammed; Mukhtar Oluwaseun Idris; Oyedele Qudus Kehinde; Ibrahim Damilare Boyenle; Ukachi Chiamaka Divine; Ibrahim Olaide Adedotun; Ajayi Ayodeji Folorunsho; Oladipo Elijah Kolawole
Journal:  In Silico Pharmacol       Date:  2021-06-25
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