Synthetically Accessible Virtual Inventory (SAVI)

Patel, Hitesh; Ihlenfeldt, Wolf‐D.; Judson, Philip N.; Ys, Moroz; Pevzner, Yuri; Peach, Megan L.; Tarasova, Nadya I.; Nicklaus, Marc C.

doi:10.26434/chemrxiv.12185559.v1

Cited by 11 publications

(11 citation statements)

References 84 publications

(77 reference statements)

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“…Pre-defined ZINC [13], ChEMBL [14], PubChem [15], GDB [16] DrugBank [17], Enamine REAL [18], WuXi Virtual Library [19], SAVI [20], PGVL [21], PLC [22] On-the-fly via heuristic methods…”

Section: Unconstrained Constrainedmentioning

confidence: 99%

“…Libraries are often enumerated by applying a small number (< 100) of reaction templates defining common single-step transformations to all possible combinations of starting materials [50,51,52,53] (Figure 1); recursive enumeration generates molecules accessible through multiple synthetic steps. There are numerous implementations of this approach [54] including SAVI [20], efforts within pharmaceutical companies [21,22], and efforts from commercial vendors [18,19]. As it becomes impractical to store such large numbers of compounds due to the combinatorial explosion of reaction products, these spaces may be defined only implicitly.…”

Section: Fragment-basedmentioning

confidence: 99%

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Defining and Exploring Chemical Spaces

Coley

2021

Trends in Chemistry

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Designing functional molecules with desirable properties is often a challenging, multi-objective optimization. For decades, there have been computational approaches to facilitate this process through the simulation of physical processes, the prediction of molecular properties using structure-property relationships, and the selection or generation of molecular structures. This piece provides an overview of some algorithmic approaches to defining and exploring chemical spaces that have the potential to operationalize the process of molecular discovery. We emphasize the potential roles of machine learning and consideration of synthetic feasibility, which is a prerequisite to "closing the loop". We conclude by summarizing important directions for the future development and evaluation of these methods.

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“…Pre-defined ZINC [13], ChEMBL [14], PubChem [15], GDB [16] DrugBank [17], Enamine REAL [18], WuXi Virtual Library [19], SAVI [20], PGVL [21], PLC [22] On-the-fly via heuristic methods…”

Section: Unconstrained Constrainedmentioning

confidence: 99%

Section: Fragment-basedmentioning

confidence: 99%

Defining and Exploring Chemical Spaces

Coley

2021

Trends in Chemistry

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“…In 2014, the NCI/NIH Computer-Aided Drug Design (CADD) Group brought together a team of researchers from both academy and industry to launch a project to create the SAVI (Synthetically Accessible Virtual Inventory) library. SAVI is a vast virtual library of new molecules predicted to be easily synthesizable and contains among other data, information on the synthetic routes and the available reagents [49,59,60].…”

Section: Identification Of New Potential Anti-hiv Agents By Analysis mentioning

confidence: 99%

“…For this release, 53 transforms were applied to approximately 150,000 building blocks in single-step reactions. A more detailed description of the SAVI project is presented in [49,59,60].…”

Section: Identification Of New Potential Anti-hiv Agents By Analysis mentioning

confidence: 99%

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

Druzhilovskiy

Stolbov

Savosina

et al. 2020

JSFI

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To improve the discovery of more effective and less toxic pharmaceutical agents, large virtual repositories of synthesizable molecules have been generated to increase the explored chemicalpharmacological space diversity. Such libraries include billions of structural formulae of druglike molecules associated with data on synthetic schemes, required building blocks, estimated physical-chemical parameters, etc. Clearly, such repositories are "Big Data". Thus, to identify the most promising compounds with the required pharmacological properties (hits) among billions of available opportunities, special computational methods are necessary. We have proposed using a combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. Our approach has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI), a 1.75 billion compound database. Potential inhibitors of HIV-1 protease and reverse transcriptase and agonists of toll-like receptors and STING, affecting innate immunity, were computationally identified. The activity of the three synthesized compounds has been confirmed in a cell-based assay. These compounds belong to the chemical classes, in which the agonistic effect on TLR 7/8 had not been previously shown. Synthesis and biological testing of several dozens of compounds with predicted antiretroviral activity are currently taking place at the NCI/NIH. We also carried out virtual screening among one billion substances to find compounds potentially possessing anti-SARS-CoV-2 activity. The selected hits' information has been accepted by the European Initiative "JEDI Grand Challenge against COVID-19" for synthesis and further biological evaluation. The possibilities and limitations of the approach are discussed.

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“…The original version covered much of organic chemistry and it was quickly adopted and used by chemists and scientific databases throughout the world. Its application in virtual databases includes SAVI-2020, a database of reactions, labelled by RInChI, with over 1.75 billion products, all of which are assigned InChI [8]. The effective coverage of chemistry had a downside: the pressure to extend and add features and capabilities was rather small.…”

Section: Introductionmentioning

confidence: 99%

InChI version 1.06: now more than 99.99% reliable

Goodman¹,

Pletnev

Thiessen

et al. 2021

J Cheminform

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The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier.

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Synthetically Accessible Virtual Inventory (SAVI)

Cited by 11 publications

References 84 publications

Defining and Exploring Chemical Spaces

Defining and Exploring Chemical Spaces

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

InChI version 1.06: now more than 99.99% reliable

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