Literature DB >> 35783295

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

Suzanne Ackloo1, Rima Al-Awar2,3, Rommie E Amaro4,5, Cheryl H Arrowsmith1, Hatylas Azevedo6, Robert A Batey7, Yoshua Bengio8, Ulrich A K Betz9, Cristian G Bologa10, John D Chodera11, Wendy D Cornell12, Ian Dunham13,14, Gerhard F Ecker15, Kristina Edfeldt16, Aled M Edwards1, Michael K Gilson5,17, Claudia R Gordijo1, Gerhard Hessler18, Alexander Hillisch19, Anders Hogner20, John J Irwin21, Johanna M Jansen22, Daniel Kuhn23, Andrew R Leach13,14, Alpha A Lee24,25, Uta Lessel26, Maxwell R Morgan1, John Moult27,28, Ingo Muegge29, Tudor I Oprea10,30, Benjamin G Perry31, Patrick Riley32, Sophie A L Rousseaux7, Kumar Singh Saikatendu33, Vijayaratnam Santhakumar1, Matthieu Schapira1,3, Cora Scholten34, Matthew H Todd35, Masoud Vedadi1,3, Andrea Volkamer36, Timothy M Willson37.   

Abstract

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Entities:  

Year:  2022        PMID: 35783295      PMCID: PMC9246350          DOI: 10.1038/s41570-022-00363-z

Source DB:  PubMed          Journal:  Nat Rev Chem        ISSN: 2397-3358            Impact factor:   34.571


  31 in total

1.  Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties.

Authors:  P Ertl; B Rohde; P Selzer
Journal:  J Med Chem       Date:  2000-10-05       Impact factor: 7.446

2.  A specific mechanism of nonspecific inhibition.

Authors:  Susan L McGovern; Brian T Helfand; Brian Feng; Brian K Shoichet
Journal:  J Med Chem       Date:  2003-09-25       Impact factor: 7.446

3.  Application of belief theory to similarity data fusion for use in analog searching and lead hopping.

Authors:  Steven W Muchmore; Derek A Debe; James T Metz; Scott P Brown; Yvonne C Martin; Philip J Hajduk
Journal:  J Chem Inf Model       Date:  2008-04-17       Impact factor: 4.956

4.  A large-scale experiment to assess protein structure prediction methods.

Authors:  J Moult; J T Pedersen; R Judson; K Fidelis
Journal:  Proteins       Date:  1995-11

5.  Computational chemistry and drug discovery: a call to action.

Authors:  Johanna M Jansen; Rommie E Amaro; Wendy Cornell; Y Jane Tseng; W Patrick Walters
Journal:  Future Med Chem       Date:  2012-10       Impact factor: 3.808

6.  D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

Authors:  Zied Gaieb; Shuai Liu; Symon Gathiaka; Michael Chiu; Huanwang Yang; Chenghua Shao; Victoria A Feher; W Patrick Walters; Bernd Kuhn; Markus G Rudolph; Stephen K Burley; Michael K Gilson; Rommie E Amaro
Journal:  J Comput Aided Mol Des       Date:  2017-12-04       Impact factor: 3.686

7.  ZINC20-A Free Ultralarge-Scale Chemical Database for Ligand Discovery.

Authors:  John J Irwin; Khanh G Tang; Jennifer Young; Chinzorig Dandarchuluun; Benjamin R Wong; Munkhzul Khurelbaatar; Yurii S Moroz; John Mayfield; Roger A Sayle
Journal:  J Chem Inf Model       Date:  2020-10-29       Impact factor: 4.956

8.  Ultra-large library docking for discovering new chemotypes.

Authors:  Jiankun Lyu; Sheng Wang; Trent E Balius; Isha Singh; Anat Levit; Yurii S Moroz; Matthew J O'Meara; Tao Che; Enkhjargal Algaa; Kateryna Tolmachova; Andrey A Tolmachev; Brian K Shoichet; Bryan L Roth; John J Irwin
Journal:  Nature       Date:  2019-02-06       Impact factor: 49.962

9.  Accurate prediction of protein structures and interactions using a three-track neural network.

Authors:  Minkyung Baek; Frank DiMaio; Ivan Anishchenko; Justas Dauparas; Sergey Ovchinnikov; Gyu Rie Lee; Jue Wang; Qian Cong; Lisa N Kinch; R Dustin Schaeffer; Claudia Millán; Hahnbeom Park; Carson Adams; Caleb R Glassman; Andy DeGiovanni; Jose H Pereira; Andria V Rodrigues; Alberdina A van Dijk; Ana C Ebrecht; Diederik J Opperman; Theo Sagmeister; Christoph Buhlheller; Tea Pavkov-Keller; Manoj K Rathinaswamy; Udit Dalwadi; Calvin K Yip; John E Burke; K Christopher Garcia; Nick V Grishin; Paul D Adams; Randy J Read; David Baker
Journal:  Science       Date:  2021-07-15       Impact factor: 47.728

10.  Target 2035 - update on the quest for a probe for every protein.

Authors:  Susanne Müller; Suzanne Ackloo; Arij Al Chawaf; Bissan Al-Lazikani; Albert Antolin; Jonathan B Baell; Hartmut Beck; Shaunna Beedie; Ulrich A K Betz; Gustavo Arruda Bezerra; Paul E Brennan; David Brown; Peter J Brown; Alex N Bullock; Adrian J Carter; Apirat Chaikuad; Mathilde Chaineau; Alessio Ciulli; Ian Collins; Jan Dreher; David Drewry; Kristina Edfeldt; Aled M Edwards; Ursula Egner; Stephen V Frye; Stephen M Fuchs; Matthew D Hall; Ingo V Hartung; Alexander Hillisch; Stephen H Hitchcock; Evert Homan; Natarajan Kannan; James R Kiefer; Stefan Knapp; Milka Kostic; Stefan Kubicek; Andrew R Leach; Sven Lindemann; Brian D Marsden; Hisanori Matsui; Jordan L Meier; Daniel Merk; Maurice Michel; Maxwell R Morgan; Anke Mueller-Fahrnow; Dafydd R Owen; Benjamin G Perry; Saul H Rosenberg; Kumar Singh Saikatendu; Matthieu Schapira; Cora Scholten; Sujata Sharma; Anton Simeonov; Michael Sundström; Giulio Superti-Furga; Matthew H Todd; Claudia Tredup; Masoud Vedadi; Frank von Delft; Timothy M Willson; Georg E Winter; Paul Workman; Cheryl H Arrowsmith
Journal:  RSC Med Chem       Date:  2021-12-03
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  1 in total

Review 1.  Protein-Ligand Docking in the Machine-Learning Era.

Authors:  Chao Yang; Eric Anthony Chen; Yingkai Zhang
Journal:  Molecules       Date:  2022-07-18       Impact factor: 4.927

  1 in total

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