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Molecules
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Fragment-to-Lead Optimization in Drug Discovery
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Fragment-to-Lead Optimization in Drug Discovery

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 8530

Special Issue Editors

Dr. Flavio Ballante

E-Mail Website
Guest Editor
Department of Cell and Molecular Biology-BMC, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
Interests: drug design and discovery; molecular recognition; medicinal chemistry; structure-activity relationship; molecular modeling; GPCR; epigenetics; chemical space; virtual screening; cheminformatics
Dr. Péter Ábrányi-Balogh

E-Mail Website
Guest Editor
Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
Interests: chemical probes; covalent labelling of proteins; antibody-conjugates; new multicomponent reactions; beta-carboline and isoquinoline alkaloids; reaction mechanisms; organophosphorous compounds

Special Issue Information

Dear Colleagues,

Fragment-based lead discovery (FBLD) has developed remarkably in the last two decades, becoming an effective approach for the identification of lead compounds and a complementary method to high throughput screening in drug discovery. FBLD aims for the detection of reversible and irreversible small molecules (fragments) binding to a biological target and their optimization to higher affinity compounds (leads). This approach provides many advantages for the identification of optimal starting points for lead development, such as efficient chemical space exploration and high fragment hit rates. A major challenge in FBLD is the transition from fragment hits to leads which requires efficient methodologies for fragment elaboration into high affinity ligands. Several approaches based on computational and experimental methods have successfully driven the optimization of fragments to viable lead series for different molecular targets and their improvement will be fundamental to broaden the application and success of FBLD in drug discovery. This Special Issue on Molecules aims to provide a venue for current research and state-of-the-art developments for lead generation from fragments. Reviews and original research articles focusing on any aspect of fragment-to-lead optimization are welcome.

Dr. Flavio Ballante
Dr. Péter Ábrányi-Balogh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fragment-to-lead optimization
  • fragment-based lead discovery
  • chemical space
  • biophysics
  • biochemistry
  • computational chemistry
  • molecular modelling
  • virtual screening
  • chemical library design
  • machine learning
  • de novo design
  • fragments
  • covalent fragments
  • drug design
  • structure-based drug design
  • X-ray crystallography
  • screening assays
  • optimization

Published Papers (3 papers)

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Research

19 pages, 2307 KiB  
Article
Green Drug Discovery: Novel Fragment Space from the Biomass-Derived Molecule Dihydrolevoglucosenone (CyreneTM)
by Tom Dekker, Jaap W. Harteveld, Gábor Wágner, Max C. M. de Vries, Hans Custers, Andrea C. van de Stolpe, Iwan J. P. de Esch and Maikel Wijtmans
Molecules 2023, 28(4), 1777; https://doi.org/10.3390/molecules28041777 - 13 Feb 2023
Cited by 1 | Viewed by 2469
Abstract
Biomass-derived molecules can provide a basis for sustainable drug discovery. However, their full exploration is hampered by the dominance of millions of old-fashioned screening compounds in classical high-throughput screening (HTS) libraries frequently utilized. We propose a fragment-based drug discovery (FBDD) approach as an [...] Read more.
Biomass-derived molecules can provide a basis for sustainable drug discovery. However, their full exploration is hampered by the dominance of millions of old-fashioned screening compounds in classical high-throughput screening (HTS) libraries frequently utilized. We propose a fragment-based drug discovery (FBDD) approach as an efficient method to navigate biomass-derived drug space. Here, we perform a proof-of-concept study with dihydrolevoglucosenone (CyreneTM), a pyrolysis product of cellulose. Diverse synthetic routes afforded a 100-membered fragment library with a diversity in functional groups appended. The library overall performs well in terms of novelty, physicochemical properties, aqueous solubility, stability, and three-dimensionality. Our study suggests that Cyrene-based fragments are a valuable green addition to the drug discovery toolbox. Our findings can help in paving the way for new hit drug candidates that are based on renewable resources. Full article
(This article belongs to the Special Issue Fragment-to-Lead Optimization in Drug Discovery)
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17 pages, 4964 KiB  
Article
In Vitro and In Silico Studies of Human Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibition by Stereoisomeric Forms of Lipophilic Nucleosides: The Role of Carbohydrate Stereochemistry in Ligand-Enzyme Interactions
by Nadezhda S. Dyrkheeva, Irina A. Chernyshova, Georgy A. Ivanov, Yuri B. Porozov, Anastasia A. Zenchenko, Vladimir E. Oslovsky, Alexandra L. Zakharenko, Darina I. Nasyrova, Galina N. Likhatskaya, Sergey N. Mikhailov, Olga I. Lavrik and Mikhail S. Drenichev
Molecules 2022, 27(8), 2433; https://doi.org/10.3390/molecules27082433 - 9 Apr 2022
Cited by 2 | Viewed by 2064
Abstract
Inhibition of human DNA repair enzyme tyrosyl-DNA phosphodiesterase 1 (Tdp1) by different chiral lipophilic nucleoside derivatives was studied. New Tdp1 inhibitors were found in the series of the studied compounds with IC50 = 2.7–6.7 μM. It was shown that D-lipophilic nucleoside derivatives [...] Read more.
Inhibition of human DNA repair enzyme tyrosyl-DNA phosphodiesterase 1 (Tdp1) by different chiral lipophilic nucleoside derivatives was studied. New Tdp1 inhibitors were found in the series of the studied compounds with IC50 = 2.7–6.7 μM. It was shown that D-lipophilic nucleoside derivatives manifested higher inhibition activity than their L-analogs, and configuration of the carbohydrate moiety can influence the mechanism of Tdp1 inhibition. Full article
(This article belongs to the Special Issue Fragment-to-Lead Optimization in Drug Discovery)
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19 pages, 2704 KiB  
Article
In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions
by Dmitry A. Shulga, Nikita N. Ivanov and Vladimir A. Palyulin
Molecules 2022, 27(6), 1985; https://doi.org/10.3390/molecules27061985 - 18 Mar 2022
Cited by 4 | Viewed by 2588
Abstract
The notion of a contribution of a specific group in an organic molecule’s property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a [...] Read more.
The notion of a contribution of a specific group in an organic molecule’s property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)—an analog of ligand efficiency (LE) for a group in the molecule—using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm. Full article
(This article belongs to the Special Issue Fragment-to-Lead Optimization in Drug Discovery)
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