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Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures
1
–
4
and ultra-large libraries of virtual compounds
5
,
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. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries
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, new approaches to compound screening are needed
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,
9
. Here we introduce a modular synthon-based approach—V-SYNTHES—to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold–synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (
K
i
) = 0.9 nM) and CB
2
/CB
1
selectivity (50–200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.
V-SYNTHES, a scalable and computationally cost-effective synthon-based approach to compound screening, identified compounds with a high affinity for CB2 and CB1 in a hierarchical structure-based screen of more than 11 billion compounds.
Journal Article
Modeling the expansion of virtual screening libraries
Recently, ‘tangible’ virtual libraries have made billions of molecules readily available. Prioritizing these molecules for synthesis and testing demands computational approaches, such as docking. Their success may depend on library diversity, their similarity to bio-like molecules and how receptor fit and artifacts change with library size. We compared a library of 3 million ‘in-stock’ molecules with billion-plus tangible libraries. The bias toward bio-like molecules in the tangible library decreases 19,000-fold versus those ‘in-stock’. Similarly, thousands of high-ranking molecules, including experimental actives, from five ultra-large-library docking campaigns are also dissimilar to bio-like molecules. Meanwhile, better-fitting molecules are found as the library grows, with the score improving log-linearly with library size. Finally, as library size increases, so too do rare molecules that rank artifactually well. Although the nature of these artifacts changes from target to target, the expectation of their occurrence does not, and simple strategies can minimize their impact.
Docking virtual libraries against protein structures has identified potent ligands for multiple targets. A comprehensive analysis reveals that the increased size of virtual libraries improves receptor fit but diverges from bio-like molecules.
Journal Article
The high-impact digital library : innovative approaches for outreach and instruction
\"This book explores background information on outreach and instruction efforts by digital library practitioners, detailed survey results from practitioners themselves, and instructional ideas such as drop-in class sessions, course-integrated instruction, training, and ways digital library practitioners can contribute to the Open Educational Resources (OER) and open pedagogy movements\"-- Provided by publisher.
BOOK
COVID-19 and digital library services – a case study of a university library
Purpose
The purpose of this paper is to share the experience of a university library in response to the COVID-19 pandemic since early March 2020. The paper describes the library’s position during the crisis and illustrates the uncharted challenges that the pandemic has posed to its digital services. Furthermore, it details how the library has adapted some existing services into a digital format and explored new initiatives/practices to support the university’s full online teaching and learning since March 23, 2020.
Design/methodology/approach
This paper describes the library’s various digital services that are used to meet the needs of its end-users during the COVID-19 pandemic. The approaches used are the authors’ personal experiences working at an academic library, observations of the library’s responses with regards to its digital services, as well as their reflections on what can be considered for development now and in the future. It highlights the current initiatives and best practices for digital library services during a public health crisis.
Findings
This paper aims to make other university libraries aware of what the library has implemented with providing digital services to its teaching faculty and students during the pandemic. It also describes the challenges and implications for the library professionals working in-house and remotely.
Originality/value
This paper is of great value in providing insights and practical solutions responding to the global health crisis for other libraries that are coping with the similar challenges for digital library services.
Journal Article
How to build a digital library
How to Build a Digital Library reviews knowledge and tools to construct and maintain a digital library, regardless of the size or purpose.A resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries.The Second Edition reflects developments in the field as well as in.
eBook
Evaluating author name disambiguation for digital libraries: a case of DBLP
Author name ambiguity in a digital library may affect the findings of research that mines authorship data of the library. This study evaluates author name disambiguation in DBLP, a widely used but insufficiently evaluated digital library for its disambiguation performance. In doing so, this study takes a triangulation approach that author name disambiguation for a digital library can be better evaluated when its performance is assessed on multiple labeled datasets with comparison to baselines. Tested on three types of labeled data containing 5000 to 6 M disambiguated names, DBLP is shown to assign author names quite accurately to distinct authors, resulting in pairwise precision, recall, and F1 measures around 0.90 or above overall. DBLP’s author name disambiguation performs well even on large ambiguous name blocks but deficiently on distinguishing authors with the same names. Compared to other disambiguation algorithms, DBLP’s disambiguation performance is quite competitive, possibly due to its hybrid disambiguation approach combining algorithmic disambiguation and manual error correction. A discussion follows on strengths and weaknesses of labeled datasets used in this study for future efforts to evaluate author name disambiguation on a digital library scale.
Journal Article