Literature DB >> 27286472

The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space.

Christos A Nicolaou1, Ian A Watson1, Hong Hu1, Jibo Wang1.   

Abstract

Venturing into the immensity of the small molecule universe to identify novel chemical structure is a much discussed objective of many methods proposed by the chemoinformatics community. To this end, numerous approaches using techniques from the fields of computational de novo design, virtual screening and reaction informatics, among others, have been proposed. Although in principle this objective is commendable, in practice there are several obstacles to useful exploitation of the chemical space. Prime among them are the sheer number of theoretically feasible compounds and the practical concern regarding the synthesizability of the chemical structures conceived using in silico methods. We present the Proximal Lilly Collection initiative implemented at Eli Lilly and Co. with the aims to (i) define the chemical space of small, drug-like compounds that could be synthesized using in-house resources and (ii) facilitate access to compounds in this large space for the purposes of ongoing drug discovery efforts. The implementation of PLC relies on coupling access to available synthetic knowledge and resources with chemo/reaction informatics techniques and tools developed for this purpose. We describe in detail the computational framework supporting this initiative and elaborate on the characteristics of the PLC virtual collection of compounds. As an example of the opportunities provided to drug discovery researchers by easy access to a large, realistically feasible virtual collection such as the PLC, we describe a recent application of the technology that led to the discovery of selective kinase inhibitors.

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Year:  2016        PMID: 27286472     DOI: 10.1021/acs.jcim.6b00173

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  12 in total

1.  Idea2Data: Toward a New Paradigm for Drug Discovery.

Authors:  Christos A Nicolaou; Christine Humblet; Hong Hu; Eva M Martin; Frank C Dorsey; Thomas M Castle; Keith Ian Burton; Haitao Hu; Jorg Hendle; Michael J Hickey; Joel Duerksen; Jibo Wang; Jon A Erickson
Journal:  ACS Med Chem Lett       Date:  2019-02-04       Impact factor: 4.345

Review 2.  Automating drug discovery.

Authors:  Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2017-12-15       Impact factor: 84.694

3.  Comparison of Large Chemical Spaces.

Authors:  Uta Lessel; Christian Lemmen
Journal:  ACS Med Chem Lett       Date:  2019-09-11       Impact factor: 4.345

4.  Algorithm for the Pruning of Synthesis Graphs.

Authors:  Gergely Zahoránszky-Kőhalmi; Nikita Lysov; Ilia Vorontcov; Jeffrey Wang; Jeyaraman Soundararajan; Dimitrios Metaxotos; Biju Mathew; Rafat Sarosh; Samuel G Michael; Alexander G Godfrey
Journal:  J Chem Inf Model       Date:  2022-04-19       Impact factor: 6.162

Review 5.  Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

Authors:  Nicholas Ekow Thomford; Dimakatso Alice Senthebane; Arielle Rowe; Daniella Munro; Palesa Seele; Alfred Maroyi; Kevin Dzobo
Journal:  Int J Mol Sci       Date:  2018-05-25       Impact factor: 5.923

6.  A Perspective on Innovating the Chemistry Lab Bench.

Authors:  Alexander G Godfrey; Samuel G Michael; Gurusingham Sitta Sittampalam; Gergely Zahoránszky-Köhalmi
Journal:  Front Robot AI       Date:  2020-02-25

7.  Accelerating high-throughput virtual screening through molecular pool-based active learning.

Authors:  David E Graff; Eugene I Shakhnovich; Connor W Coley
Journal:  Chem Sci       Date:  2021-04-29       Impact factor: 9.825

8.  ChemOS: An orchestration software to democratize autonomous discovery.

Authors:  Loïc M Roch; Florian Häse; Christoph Kreisbeck; Teresa Tamayo-Mendoza; Lars P E Yunker; Jason E Hein; Alán Aspuru-Guzik
Journal:  PLoS One       Date:  2020-04-16       Impact factor: 3.240

9.  SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

Authors:  Hitesh Patel; Wolf-Dietrich Ihlenfeldt; Philip N Judson; Yurii S Moroz; Yuri Pevzner; Megan L Peach; Victorien Delannée; Nadya I Tarasova; Marc C Nicklaus
Journal:  Sci Data       Date:  2020-11-11       Impact factor: 6.444

10.  Generating Multibillion Chemical Space of Readily Accessible Screening Compounds.

Authors:  Oleksandr O Grygorenko; Dmytro S Radchenko; Igor Dziuba; Alexander Chuprina; Kateryna E Gubina; Yurii S Moroz
Journal:  iScience       Date:  2020-10-15
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