Cyclopentitol as a scaffold for a natural product-like compound library for drug discovery

Padwal, Jalindar; Filippov, Dmitri V.; Narhe, Bharat D.; Aertssen, Sjoerd; Beuving, Remmelt Jan; Benningshof, Jorg C. J.; Marel, Gijsbert A. van der; Overkleeft, Herman S.; Stelt, Mario van der

doi:10.1016/j.bmc.2015.01.040

Cited by 12 publications

(5 citation statements)

References 10 publications

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Section: A Brief History Of Small Molecule Designmentioning

confidence: 99%

“…37 The library was synthesized by the rapid assembly of various functional groups onto a small molecule scaffold, creating a library of derivative compounds from the given scaffold. 37 Following that, many small molecule libraries of compounds based on scaffolds were generated and tested, 38,39 and many research groups focused on scaffolds that showed activity toward multiple biological targets, termed privileged scaffolds, for their research. 40,41 Even though the rapid synthesis of scaffold-based compound libraries is many orders of magnitude faster than isolating and purifying active components from extracts in nature, the process of small molecule design still suffers from the large amount of time and effort needed to test the synthesized compounds, which limits the rate of discovery.…”

Section: ■ Introductionmentioning

confidence: 99%

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The Experimentalist’s Guide to Machine Learning for Small Molecule Design

Lindley,

Lu,

Shukla

2023

ACS Appl. Bio Mater.

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Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

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Section: A Brief History Of Small Molecule Designmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

The Experimentalist’s Guide to Machine Learning for Small Molecule Design

Lindley,

Lu,

Shukla

2023

ACS Appl. Bio Mater.

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“…Indeed, a significant number of unprecedented spiro, bridged, and fused polycycles with different degrees of saturation, conjugation, and substitution have been synthesized and expanded to a library format. This has been recently exemplified by several publications from the ELF chemistry groups detailing the associated design and validation aspects [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Given that most theoretical ring systems remains unexplored [39], the synthesis of novel rings represents one of the strategies embraced by the ELF chemistry consortium to expand the available chemical space.…”

Section: Page 5 Of 11mentioning

confidence: 99%

Expansion of chemical space for collaborative lead generation and drug discovery: the European Lead Factory Perspective

Karawajczyk¹,

Giordanetto²,

Benningshof³

et al. 2015

Drug Discovery Today

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High-throughput screening (HTS) represents a major cornerstone of drug discovery. The availability of an innovative, relevant and high-quality compound collection to be screened often dictates the final fate of a drug discovery campaign. Given that the chemical space to be sampled in research programs is practically infinite and sparsely populated, significant efforts and resources need to be invested in the generation and maintenance of a competitive compound collection. The European Lead Factory (ELF) project is addressing this challenge by leveraging the diverse experience and know-how of academic groups and small and medium enterprises (SMEs) engaged in synthetic and/or medicinal chemistry. Here, we describe the novelty, diversity, structural complexity, physicochemical characteristics and overall attractiveness of this first batch of ELF compounds for HTS purposes.

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“…These challenges notwithstanding, the scientists of ELF Chemistry Consortium have developed key approaches that can facilitate novel bioactive molecule discovery, including diversity-oriented synthesis, biology-oriented synthesis, multicomponent chemistry and activity-directed synthesis, as recently described for selected examples [23][24][25][26][27][28][29][30][31][32][33][34] . The innovative chemical approaches taken by the ELF Chemistry Consortium are yielding novel, diverse and distinctive compounds that will complement existing large compound collections used for high throughput screening drug discovery applications, thus serving as a blueprint for future compound collection enhancement campaigns.…”

Section: Challengesmentioning

confidence: 99%

The European lead factory—an experiment in collaborative drug discovery

Laverty

Orrling

Giordanetto³

et al. 2016

J Med Dev Sci

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The European lead factory-an experiment in collaborative drug discovery. © 2015 Hugh Laverty, et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 REVIEW ARTICLEThe European lead factory-an experiment in collaborative drug discovery Abstract: The European Lead Factory (ELF) is a unique collaborative public-private partnership aiming to deliver innovative drug discovery starting points and improving the value generated by ultra-High Throughput Screening (uHTS) approaches. Composed of a unique compound collection derived from private pharmaceutical company collections and complemented with new chemistries from a unique public collection, it offers a unique uHTS platform accessible to both private companies and publicly funded researchers. One of the key challenges in setting up ELF has been to balance access to screening results with protecting the value of compounds in the collection. Through an 'honest data broker' data management platform and a royalty reward scheme based on achieved milestones, ELF has been able to overcome these challenges. Set up in 2013, it has already accepted 42 targets for screening, submitted by publicly funded researchers, and generated 12 Qualified Hit Lists. In addition, 55,000 new library compounds have been generated by the public partners and added to the 320,000 compounds made available by the companies. Although it faced many challenges in becoming operational, this unique experiment in collaboration is already generating exciting results that will hopefully, eventually lead to better medicines and tools to advance our biological knowledge, and should act as a template for future approaches in the area.

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Cyclopentitol as a scaffold for a natural product-like compound library for drug discovery

Cited by 12 publications

References 10 publications

The Experimentalist’s Guide to Machine Learning for Small Molecule Design

The Experimentalist’s Guide to Machine Learning for Small Molecule Design

Expansion of chemical space for collaborative lead generation and drug discovery: the European Lead Factory Perspective

The European lead factory—an experiment in collaborative drug discovery

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