{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:28:28Z","timestamp":1772166508049,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a\n                    <jats:italic>de novo<\/jats:italic>\n                    design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a \u201clego-building\u201d process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http:\/\/github.com\/KeenThera\/SECSE.\n                  <\/jats:p>","DOI":"10.1186\/s13321-022-00598-4","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T06:13:27Z","timestamp":1648793607000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Systemic evolutionary chemical space exploration for drug discovery"],"prefix":"10.1186","volume":"14","author":[{"given":"Chong","family":"Lu","sequence":"first","affiliation":[]},{"given":"Shien","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Weihua","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zhou","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xiaoxiao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Faji","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Xia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0742-3172","authenticated-orcid":false,"given":"Yikai","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"598_CR1","unstructured":"MADE Building blocks from Enamine. https:\/\/enamine.net\/building-blocks\/make-on-demand-building-blocks. Accessed 1 Dec 2021"},{"issue":"7743","key":"598_CR2","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","volume":"566","author":"J Lyu","year":"2019","unstructured":"Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O\u2019Meara MJ, Che T, Algaa E, Tolmachova K, Tolmachev AA, Shoichet BK, Roth BL, Irwin JJ (2019) Ultra-large library docking for discovering new chemotypes. Nature 566(7743):224\u2013229. https:\/\/doi.org\/10.1038\/s41586-019-0917-9","journal-title":"Nature"},{"key":"598_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41596-021-00597-z","volume":"16","author":"BJ Bender","year":"2021","unstructured":"Bender BJ, Gahbauer S, Luttens A, Lyu J, Webb CM, Stein RM, Fink EA, Balius TE, Carlsson J, Irwin JJ,\u00a0Shoichet BK (2021) A practical guide to large-scale docking. Nat protoc 16:1\u201334","journal-title":"Nat protoc"},{"key":"598_CR4","doi-asserted-by":"crossref","unstructured":"Warr W (2021). Report on an NIH Workshop on Ultralarge Chemistry Databases. ChemRxiv.https:\/\/doi.org\/10.26434\/chemrxiv.14554803.v1","DOI":"10.26434\/chemrxiv.14554803"},{"key":"598_CR5","unstructured":"BioSolveIT: Efficient 3D exploration of multi-billion compound spaces. BioSolveIT. https:\/\/cactus.nci.nih.gov\/presentations\/NIHBigDB_2020-12\/ChristianLemmen4NIHworkshop.pdf.\u00a0Accessed 01 Dec 2021"},{"issue":"1","key":"598_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6","volume":"16","author":"RS Bohacek","year":"1996","unstructured":"Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16(1):3\u201350. https:\/\/doi.org\/10.1002\/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6","journal-title":"Med Res Rev"},{"issue":"8","key":"598_CR7","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1038\/nrd1799","volume":"4","author":"G Schneider","year":"2005","unstructured":"Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4(8):649\u2013663. https:\/\/doi.org\/10.1038\/nrd1799","journal-title":"Nat Rev Drug Discov"},{"key":"598_CR8","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-1-60761-839-3_12","volume-title":"De novo drug design","author":"M Hartenfeller","year":"2011","unstructured":"Hartenfeller M, Schneider G, Bajorath J (2011) De novo drug design. Humana Press, Totowa, pp 299\u2013323. https:\/\/doi.org\/10.1007\/978-1-60761-839-3_12"},{"issue":"32","key":"598_CR9","doi-asserted-by":"publisher","first-page":"10792","DOI":"10.1002\/anie.201814681","volume":"58","author":"G Schneider","year":"2019","unstructured":"Schneider G, Clark DE (2019) Automated de novo drug design: are we nearly there yet? Angew Chem Int Ed 58(32):10792\u201310803. https:\/\/doi.org\/10.1002\/anie.201814681","journal-title":"Angew Chem Int Ed"},{"issue":"4","key":"598_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijms22041676","volume":"22","author":"VD Mouchlis","year":"2021","unstructured":"Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, Lynch I, Greco D, Melagraki G (2021) Advances in de novo drug design: From conventional to machine learning methods. Int J Mol Sci 22(4):1\u201322. https:\/\/doi.org\/10.3390\/ijms22041676","journal-title":"Int J Mol Sci"},{"issue":"24","key":"598_CR11","doi-asserted-by":"publisher","first-page":"8362","DOI":"10.1039\/d1sc01050f","volume":"12","author":"O Dollar","year":"2021","unstructured":"Dollar O, Joshi N, Beck DAC, Pfaendtner J (2021) Attention-based generative models for: de novo molecular design. Chem Sci 12(24):8362\u20138372. https:\/\/doi.org\/10.1039\/d1sc01050f","journal-title":"Chem Sci"},{"issue":"6","key":"598_CR12","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/BF00126217","volume":"6","author":"HJ B\u00f6hm","year":"1992","unstructured":"B\u00f6hm HJ (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6(6):593\u2013606. https:\/\/doi.org\/10.1007\/BF00126217","journal-title":"J Comput Aided Mol Des"},{"issue":"7\u20138","key":"598_CR13","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1007\/s0089400060498","volume":"6","author":"R Wang","year":"2000","unstructured":"Wang R, Gao Y, Lai L (2000) LigBuilder: a multi-purpose program for structure-based drug design. J Mol Model 6(7\u20138):498\u2013516. https:\/\/doi.org\/10.1007\/s0089400060498","journal-title":"J Mol Model"},{"issue":"6","key":"598_CR14","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1021\/ci600246s","volume":"46","author":"J Chen","year":"2006","unstructured":"Chen J, Lai L (2006) Pocket vol 2: further developments on receptor-based pharmacophore modeling. Journal of Chem Inform Model 46(6):2684\u20132691. https:\/\/doi.org\/10.1021\/ci600246s","journal-title":"Journal of Chem Inform Model"},{"issue":"5","key":"598_CR15","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.3389\/fchem.2020.00142","volume":"8","author":"Y Yuan","year":"2020","unstructured":"Yuan Y, Pei J, Lai L (2020) LigBuilder V3: a multi-target de novo drug design approach. Front Chem 8(5):1083\u20131091. https:\/\/doi.org\/10.3389\/fchem.2020.00142","journal-title":"Front Chem"},{"issue":"6","key":"598_CR16","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1021\/ci200036m","volume":"51","author":"Y Li","year":"2011","unstructured":"Li Y, Zhao Y, Liu Z, Wang R (2011) Automatic tailoring and transplanting: a practical method that makes virtual screening more useful. J Chem Inform Model 51(6):1474\u20131491. https:\/\/doi.org\/10.1021\/ci200036m","journal-title":"J Chem Inform Model"},{"issue":"2","key":"598_CR17","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1021\/acs.jcim.5b00691","volume":"56","author":"Y Li","year":"2016","unstructured":"Li Y, Zhao Z, Liu Z, Su M, Wang R (2016) AutoT&T vol 2: an efficient and versatile tool for lead structure generation and optimization. J Chem Inform Model 56(2):435\u2013453. https:\/\/doi.org\/10.1021\/acs.jcim.5b00691","journal-title":"J Chem Inform Model"},{"issue":"9","key":"598_CR18","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.1021\/acs.jmedchem.5b00886","volume":"59","author":"N Ch\u00e9ron","year":"2016","unstructured":"Ch\u00e9ron N, Jasty N, Shakhnovich EI (2016) OpenGrowth: an automated and rational algorithm for finding new protein ligands. J Med Chem 59(9):4171\u20134188. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b00886","journal-title":"J Med Chem"},{"issue":"2","key":"598_CR19","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1111\/j.1747-0285.2008.00761.x","volume":"73","author":"JD Durrant","year":"2009","unstructured":"Durrant JD, Amaro RE, McCammon JA (2009) AutoGrow: a novel algorithm for protein inhibitor design. Chem Biol Drug Design 73(2):168\u2013178. https:\/\/doi.org\/10.1111\/j.1747-0285.2008.00761.x","journal-title":"Chem Biol Drug Design"},{"issue":"1","key":"598_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00429-4","volume":"12","author":"JO Spiegel","year":"2020","unstructured":"Spiegel JO, Durrant JD (2020) AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. Cheminform 12(1):1\u201316. https:\/\/doi.org\/10.1186\/s13321-020-00429-4","journal-title":"Cheminform"},{"issue":"Suppl.","key":"598_CR21","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.addr.2012.09.019","volume":"64","author":"CA Lipinski","year":"2012","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 64(Suppl.):4\u201317. https:\/\/doi.org\/10.1016\/j.addr.2012.09.019","journal-title":"Adv Drug Deliv Rev"},{"issue":"7","key":"598_CR22","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/jm901137j","volume":"53","author":"JB Baell","year":"2010","unstructured":"Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53(7):2719\u20132740. https:\/\/doi.org\/10.1021\/jm901137j","journal-title":"J Med Chem"},{"issue":"1","key":"598_CR23","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13321-020-00431-w","volume":"12","author":"P Polishchuk","year":"2020","unstructured":"Polishchuk P (2020) CReM: chemically reasonable mutations framework for structure generation. J Cheminform 12(1):28. https:\/\/doi.org\/10.1186\/s13321-020-00431-w","journal-title":"J Cheminform"},{"issue":"20","key":"598_CR24","doi-asserted-by":"publisher","first-page":"7079","DOI":"10.1039\/d1sc00231g","volume":"12","author":"A Nigam","year":"2021","unstructured":"Nigam A, Pollice R, Krenn M, Gomes GDP, Aspuru-Guzik A (2021) Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES. Chem Sci 12(20):7079\u20137090. https:\/\/doi.org\/10.1039\/d1sc00231g","journal-title":"Chem Sci"},{"key":"598_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.7717\/peerj-pchem.18","volume":"3","author":"C Steinmann","year":"2021","unstructured":"Steinmann C, Jensen JH (2021) Using a genetic algorithm to find molecules with good docking scores. PeerJ Phys Chem 3:18. https:\/\/doi.org\/10.7717\/peerj-pchem.18","journal-title":"PeerJ Phys Chem"},{"issue":"3","key":"598_CR26","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1093\/bib\/bbaa161","volume":"22","author":"Q Bai","year":"2021","unstructured":"Bai Q, Tan S, Xu T, Liu H, Huang J, Yao X (2021) MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Brief Bioinform 22(3):161. https:\/\/doi.org\/10.1093\/bib\/bbaa161","journal-title":"Brief Bioinform"},{"issue":"7","key":"598_CR27","doi-asserted-by":"publisher","first-page":"3304","DOI":"10.1021\/acs.jcim.1c00679","volume":"61","author":"B Ma","year":"2021","unstructured":"Ma B, Terayama K, Matsumoto S, Isaka Y, Sasakura Y, Iwata H, Araki M, Okuno Y (2021) Structure-based de novo molecular generator combined with artificial intelligence and docking simulations. J Chem Inform Model 61(7):3304\u20133313. https:\/\/doi.org\/10.1021\/acs.jcim.1c00679","journal-title":"J Chem Inform Model"},{"issue":"1","key":"598_CR28","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1080\/14686996.2017.1401424","volume":"18","author":"X Yang","year":"2017","unstructured":"Yang X, Zhang J, Yoshizoe K, Terayama K, Tsuda K (2017) ChemTS: an efficient python library for de novo molecular generation. Sci Technol Adv Mater 18(1):972\u2013976. https:\/\/doi.org\/10.1080\/14686996.2017.1401424","journal-title":"Sci Technol Adv Mater"},{"issue":"41","key":"598_CR29","doi-asserted-by":"publisher","first-page":"13664","DOI":"10.1039\/d1sc04444c","volume":"12","author":"Y Li","year":"2021","unstructured":"Li Y, Pei J, Lai L (2021) Structure-based de novo drug design using 3D deep generative models. Chem Sci 12(41):13664\u201313675. https:\/\/doi.org\/10.1039\/d1sc04444c","journal-title":"Chem Sci"},{"key":"598_CR30","unstructured":"Gebauer NWA, Gastegger M, Sch\u00fctt KT (2019) Symmetry-adapted generation of 3D point sets for the targeted discovery of molecules. Adv Neural Inform Process Syst. 32 (2019). https:\/\/arxiv.org\/abs\/1906.00957https:\/\/arxiv.org\/abs\/1906.00957"},{"issue":"4","key":"598_CR31","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1021\/acs.jcim.9b01120","volume":"60","author":"F Imrie","year":"2020","unstructured":"Imrie F, Bradley AR, Van Der Schaar M, Deane CM (2020) Deep generative models for 3D linker design. J Chem Inform Model 60(4):1983\u20131995. https:\/\/doi.org\/10.1021\/acs.jcim.9b01120","journal-title":"J Chem Inform Model"},{"issue":"23","key":"598_CR32","doi-asserted-by":"publisher","first-page":"8036","DOI":"10.1039\/d1sc00163a","volume":"12","author":"H Green","year":"2021","unstructured":"Green H, Koes DR, Durrant JD (2021) DeepFrag: a deep convolutional neural network for fragment-based lead optimization. Chemical Science 12(23):8036\u20138047. https:\/\/doi.org\/10.1039\/d1sc00163a","journal-title":"Chem Sci"},{"key":"598_CR33","unstructured":"Nesterov V, Wieser M, Roth V (2020) 3DMolNet: A generative network for molecular structures. arXiv. https:\/\/arxiv.org\/abs\/2010.06477"},{"issue":"20","key":"598_CR34","doi-asserted-by":"publisher","first-page":"7011","DOI":"10.1016\/j.bmc.2006.06.024","volume":"14","author":"KD Stewart","year":"2006","unstructured":"Stewart KD, Shiroda M, James CA (2006) Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorgan Med Chem 14(20):7011\u20137022. https:\/\/doi.org\/10.1016\/j.bmc.2006.06.024","journal-title":"Bioorgan Med Chem"},{"key":"598_CR35","doi-asserted-by":"crossref","unstructured":"Stewart KD, Shanley J, Ahmed KBA, Bowen JP (2012) The drug guru project, Chap. 11. vol. 54, pp 183\u2013198.  West Sussex: John Wiley. https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/9783527654307.ch11","DOI":"10.1002\/9783527654307.ch11"},{"issue":"25","key":"598_CR36","doi-asserted-by":"publisher","first-page":"8732","DOI":"10.1021\/ja902302h","volume":"131","author":"LC Blum","year":"2009","unstructured":"Blum LC, Reymond JL (2009) 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131(25):8732\u20138733. https:\/\/doi.org\/10.1021\/ja902302h","journal-title":"J Am Chem Soc"},{"key":"598_CR37","unstructured":"RDKit: Open-source cheminformatics. http:\/\/www.rdkit.org. Accessed 13 Oct 2021"},{"issue":"10","key":"598_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-2946-3-33","volume":"3","author":"NM O\u2019Boyle","year":"2011","unstructured":"O\u2019Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminform 3(10):1\u201314. https:\/\/doi.org\/10.1186\/1758-2946-3-33","journal-title":"J Cheminform"},{"issue":"D1","key":"598_CR39","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1093\/NAR\/GKAA1038","volume":"49","author":"SK Burley","year":"2021","unstructured":"Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Christie CH, Dalenberg K, Di Costanzo L, Duarte JM, Dutta S, Feng Z, Ganesan S, Goodsell DS, Ghosh S, Green RK, Guranovic V, Guzenko D, Hudson BP, Lawson CL, Liang Y, Lowe R, Namkoong H, Peisach E, Persikova I, Randle C, Rose A, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Tao YP, Voigt M, Westbrook JD, Young JY, Zardecki C, Zhuravleva M (2021) RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res 49(D1):437\u2013451. https:\/\/doi.org\/10.1093\/NAR\/GKAA1038","journal-title":"Nucleic Acids Res"},{"issue":"7873","key":"598_CR40","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, \u017d\u00eddek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583\u2013589. https:\/\/doi.org\/10.1038\/s41586-021-03819-2","journal-title":"Nature"},{"issue":"6557","key":"598_CR41","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1126\/science.abj8754","volume":"373","author":"M Baek","year":"2021","unstructured":"Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Dustin Schaeffer R, Mill\u00e1n C, Park H, Adams C, Glassman CR, DeGiovanni A, Pereira JH, Rodrigues AV, Van Dijk AA, Ebrecht AC, Opperman DJ, Sagmeister T, Buhlheller C, Pavkov-Keller T, Rathinaswamy MK, Dalwadi U, Yip CK, Burke JE, Christopher Garcia K, Grishin NV, Adams PD, Read RJ, Baker D (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373(6557):871\u2013876. https:\/\/doi.org\/10.1126\/science.abj8754","journal-title":"Science"},{"issue":"12","key":"598_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1004586","volume":"11","author":"PA Ravindranath","year":"2015","unstructured":"Ravindranath PA, Forli S, Goodsell DS, Olson AJ, Sanner MF (2015) AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput Biol 11(12):1\u201328. https:\/\/doi.org\/10.1371\/journal.pcbi.1004586","journal-title":"PLoS Comput Biol"},{"issue":"20","key":"598_CR43","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1093\/bioinformatics\/btw367","volume":"32","author":"PA Ravindranath","year":"2016","unstructured":"Ravindranath PA, Sanner MF (2016) AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms. Bioinformatics 32(20):3142\u20133149. https:\/\/doi.org\/10.1093\/bioinformatics\/btw367","journal-title":"Bioinformatics"},{"issue":"2","key":"598_CR44","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4(2):90\u201398. https:\/\/doi.org\/10.1038\/nchem.1243","journal-title":"Nat Chem"},{"key":"598_CR45","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.21334","author":"O Trott","year":"2009","unstructured":"Trott O, Olson AJ (2009) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. https:\/\/doi.org\/10.1002\/jcc.21334","journal-title":"J Comput Chem"},{"issue":"8","key":"598_CR46","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.1021\/acs.jcim.1c00203","volume":"61","author":"J Eberhardt","year":"2021","unstructured":"Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inform Model 61(8):3891\u20133898. https:\/\/doi.org\/10.1021\/acs.jcim.1c00203","journal-title":"J Chem Inform Model"},{"key":"598_CR47","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.96.18.9997","author":"ID Kuntz","year":"1999","unstructured":"Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) The maximal affinity of ligands. Tech Rep. https:\/\/doi.org\/10.1073\/pnas.96.18.9997","journal-title":"Tech Rep"},{"key":"598_CR48","unstructured":"How is Ligand Efficiency calculated? Schr\u00f6dinger, Inc. https:\/\/www.schrodinger.com\/kb\/1622. Accessed October 13, 2021"},{"key":"598_CR49","unstructured":"Chemical.AI. Wuhan Zhihua Technology Co., Ltd. https:\/\/chemical.ai. Accessed October 13 Oct 2021"},{"key":"598_CR50","unstructured":"Goh GK-m, Foster JA (1999) Evolving molecules for drug design using genetic algorithms via molecular trees. In: Proceedings of the Genetic and Evolutionary Computation Conference. pp. 27\u201333"},{"issue":"2","key":"598_CR51","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1023\/A:1022602019183","volume":"3","author":"DE Goldberg","year":"1988","unstructured":"Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95\u201399. https:\/\/doi.org\/10.1023\/A:1022602019183","journal-title":"Mach Learn"},{"key":"598_CR52","doi-asserted-by":"publisher","unstructured":"Rocke DM, Michalewicz Z (2000) Genetic Algorithms + Data Structures = Evolution Programs. vol. 95, p. 347. New York; Springer. https:\/\/doi.org\/10.2307\/2669583","DOI":"10.2307\/2669583"},{"key":"598_CR53","doi-asserted-by":"publisher","unstructured":"Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K., Barzilay, R (2019) Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling 59(8), 3370\u20133388. https:\/\/doi.org\/10.1021\/acs.jcim.9b00237. arXiv:1904.01561","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"598_CR54","doi-asserted-by":"publisher","first-page":"42","DOI":"10.5281\/zenodo.16303","volume":"36","author":"O Tange","year":"2011","unstructured":"Tange O (2011) GNU parallel - the command-line power tool. In: login. The USENIX Magazine, vol 36. pp. 42\u201347.\u00a0https:\/\/doi.org\/10.5281\/zenodo.16303","journal-title":"USENIX Mag"},{"issue":"21","key":"598_CR55","doi-asserted-by":"publisher","first-page":"6752","DOI":"10.1021\/jm901241e","volume":"52","author":"F Lovering","year":"2009","unstructured":"Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52(21):6752\u20136756. https:\/\/doi.org\/10.1021\/jm901241e","journal-title":"J Med Chem"},{"issue":"3","key":"598_CR56","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1021\/ci025599w","volume":"43","author":"WHB Sauer","year":"2003","unstructured":"Sauer WHB, Schwarz MK (2003) Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity. J Chem Inform Comput Sci 43(3):987\u20131003. https:\/\/doi.org\/10.1021\/ci025599w","journal-title":"J Chem Inform Comput Sci"},{"issue":"11","key":"598_CR57","doi-asserted-by":"publisher","first-page":"2289","DOI":"10.1016\/j.celrep.2017.05.067","volume":"19","author":"B Zhang","year":"2017","unstructured":"Zhang B, Zheng A, Hydbring P, Ambroise G, Ouchida AT, Goiny M, Vakifahmetoglu-Norberg H, Norberg E (2017) PHGDH defines a metabolic subtype in lung adenocarcinomas with poor prognosis. Cell Rep 19(11):2289\u20132303. https:\/\/doi.org\/10.1016\/j.celrep.2017.05.067","journal-title":"Cell Rep"},{"issue":"4","key":"598_CR58","doi-asserted-by":"publisher","first-page":"762","DOI":"10.20517\/cdr.2020.46","volume":"3","author":"R Rathore","year":"2020","unstructured":"Rathore R, Schutt CR, van Tine BA (2020) PHGDH as a mechanism for resistance in metabolically-driven cancers. Cancer Drug Resist 3(4):762\u2013774. https:\/\/doi.org\/10.20517\/cdr.2020.46","journal-title":"Cancer Drug Resist"},{"key":"598_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2021.113379","author":"JY Zhao","year":"2021","unstructured":"Zhao JY, Feng KR, Wang F, Zhang JW, Cheng JF, Lin GQ, Gao D, Tian P (2021) A retrospective overview of PHGDH and its inhibitors for regulating cancer metabolism. Eur J Med Chem. https:\/\/doi.org\/10.1016\/j.ejmech.2021.113379","journal-title":"Eur J Med Chem"},{"issue":"1","key":"598_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-07868-6","volume":"9","author":"MA Reid","year":"2018","unstructured":"Reid MA, Allen AE, Liu S, Liberti MV, Liu P, Liu X, Dai Z, Gao X, Wang Q, Liu Y, Lai L, Locasale JW (2018) Serine synthesis through PHGDH coordinates nucleotide levels by maintaining central carbon metabolism. Nat Commun 9(1):1\u201311. https:\/\/doi.org\/10.1038\/s41467-018-07868-6","journal-title":"Nat Commun"},{"issue":"17","key":"598_CR61","doi-asserted-by":"publisher","first-page":"2503","DOI":"10.1016\/j.bmcl.2019.07.011","volume":"29","author":"E Mullarky","year":"2019","unstructured":"Mullarky E, Xu J, Robin AD, Huggins DJ, Jennings A, Noguchi N, Olland A, Lakshminarasimhan D, Miller M, Tomita D, Michino M, Su T, Zhang G, Stamford AW, Meinke PT, Kargman S, Cantley LC (2019) Inhibition of 3-phosphoglycerate dehydrogenase (PHGDH) by indole amides abrogates de novo serine synthesis in cancer cells. Bioorg Med Chem Lett 29(17):2503\u20132510. https:\/\/doi.org\/10.1016\/j.bmcl.2019.07.011","journal-title":"Bioorg Med Chem Lett"},{"issue":"17","key":"598_CR62","doi-asserted-by":"publisher","first-page":"7976","DOI":"10.1021\/acs.jmedchem.9b00718","volume":"62","author":"H Weinstabl","year":"2019","unstructured":"...Weinstabl H, Treu M, Rinnenthal J, Zahn SK, Ettmayer P, Bader G, Dahmann G, Kessler D, Rumpel K, Mischerikow N, Savarese F, Gerstberger T, Mayer M, Zoephel A, Schnitzer R, Sommergruber W, Martinelli P, Arnhof H, Peric-Simov B, Hofbauer KS, Garavel G, Scherbantin Y, Mitzner S, Fett TN, Scholz G, Bruchhaus J, Burkard M, Kousek R, Ciftci T, Sharps B, Schrenk A, Harrer C, Haering D, Wolkerstorfer B, Zhang X, Lv X, Du A, Li D, Li Y, Quant J, Pearson M, McConnell DB (2019) Intracellular trapping of the selective phosphoglycerate dehydrogenase (PHGDH) inhibitor BI-4924 disrupts serine biosynthesis. J Med Chem 62(17):7976\u20137997. https:\/\/doi.org\/10.1021\/acs.jmedchem.9b00718","journal-title":"J Med Chem"},{"issue":"17","key":"598_CR63","doi-asserted-by":"publisher","first-page":"13139","DOI":"10.18632\/ONCOTARGET.11487","volume":"9","author":"JE Unterlass","year":"2016","unstructured":"Unterlass JE, Basl\u00e9 A, Blackburn TJ, Tucker J, Cano C, Noble MEM, Curtin NJ, Unterlass JE, Basl\u00e9 A, Blackburn TJ, Tucker J, Cano C, Noble MEM, Curtin NJ (2016) Validating and enabling phosphoglycerate dehydrogenase (PHGDH) as a target for fragment-based drug discovery in PHGDH-amplified breast cancer. Oncotarget 9(17):13139\u201313153. https:\/\/doi.org\/10.18632\/ONCOTARGET.11487","journal-title":"Oncotarget"},{"key":"598_CR64","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.1c00810","author":"Y Yang","year":"2021","unstructured":"Yang Y, Yao K, Repasky MP, Leswing K, Abel R, Shoichet BK, Jerome SV (2021) Efficient exploration of chemical space with docking and deep learning. J Chem Theory Comput. https:\/\/doi.org\/10.1021\/acs.jctc.1c00810","journal-title":"J Chem Theory Comput"},{"issue":"6","key":"598_CR65","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1021\/acscentsci.0c00229","volume":"6","author":"F Gentile","year":"2020","unstructured":"Gentile F, Agrawal V, Hsing M, Ton AT, Ban F, Norinder U, Gleave ME, Cherkasov A (2020) Deep docking: a deep learning platform for augmentation of structure based drug discovery. ACS Cent Sci 6(6):939\u2013949. https:\/\/doi.org\/10.1021\/acscentsci.0c00229","journal-title":"ACS Cent Sci"},{"key":"598_CR66","doi-asserted-by":"publisher","DOI":"10.3390\/ijms222111635","author":"J Choi","year":"2021","unstructured":"Choi J, Lee J (2021) V- dock: fast generation of novel drug-like molecules using machine-learning-based docking score and molecular optimization. Int J Mol Sci. https:\/\/doi.org\/10.3390\/ijms222111635","journal-title":"Int J Mol Sci"},{"issue":"10","key":"598_CR67","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1002\/anie.200462457","volume":"44","author":"T Fink","year":"2005","unstructured":"Fink T, Bruggesser H, Reymond JL (2005) Virtual exploration of the small-molecule chemical universe below 160 daltons. Angew Chem Int Ed 44(10):1504\u20131508. https:\/\/doi.org\/10.1002\/anie.200462457","journal-title":"Angew Chem Int Ed"},{"issue":"11","key":"598_CR68","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1021\/ci300415d","volume":"52","author":"L Ruddigkeit","year":"2012","unstructured":"Ruddigkeit L, Van Deursen R, Blum LC, Reymond JL (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inform Model 52(11):2864\u20132875. https:\/\/doi.org\/10.1021\/ci300415d","journal-title":"J Chem Inform Model"},{"key":"598_CR69","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01496","author":"T Sousa","year":"2021","unstructured":"Sousa T, Correia J, Pereira V, Rocha M (2021) Generative deep learning for targeted compound design. J Chem Inform Model. https:\/\/doi.org\/10.1021\/acs.jcim.0c01496","journal-title":"J Chem Inform Model"},{"key":"598_CR70","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.ddtec.2020.09.003","volume":"32\u201333","author":"P Renz","year":"2019","unstructured":"Renz P, Van Rompaey D, Wegner JK, Hochreiter S, Klambauer G (2019) On failure modes in molecule generation and optimization. Drug Discov Today Technol 32\u201333:55\u201363. https:\/\/doi.org\/10.1016\/j.ddtec.2020.09.003","journal-title":"Drug Discov Today Technol"},{"issue":"8","key":"598_CR71","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1021\/ci100084s","volume":"50","author":"DJ Warner","year":"2010","unstructured":"Warner DJ, Griffen EJ, St-Gallay SA (2010) WizePairZ: A novel algorithm to identify, encode, and exploit matched molecular pairs with unspecified cores in medicinal chemistry. J Chem Inform Model 50(8):1350\u20131357. https:\/\/doi.org\/10.1021\/ci100084s","journal-title":"J Chem Inform Model"},{"issue":"3","key":"598_CR72","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1021\/ci900450m","volume":"50","author":"J Hussain","year":"2010","unstructured":"Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inform Model 50(3):339\u2013348. https:\/\/doi.org\/10.1021\/ci900450m","journal-title":"J Chem Inform Model"},{"issue":"2","key":"598_CR73","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1021\/acs.jcim.0c01143","volume":"61","author":"M Awale","year":"2021","unstructured":"Awale M, Hert J, Guasch L, Riniker S, Kramer C (2021) The playbooks of medicinal chemistry design moves. J Chem Inform Model 61(2):729\u2013742. https:\/\/doi.org\/10.1021\/acs.jcim.0c01143","journal-title":"J Chem Inform Model"},{"issue":"D1","key":"598_CR74","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1093\/nar\/gkaa920","volume":"49","author":"T Yang","year":"2021","unstructured":"Yang T, Li Z, Chen Y, Feng D, Wang G, Fu Z, Ding X, Tan X, Zhao J, Luo X, Chen K, Jiang H, Zheng M (2021) DrugSpaceX: a large screenable and synthetically tractable database extending drug space. Nucleic Acids Res 49(D1):1170\u20131178. https:\/\/doi.org\/10.1093\/nar\/gkaa920","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"598_CR75","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s10822-019-00234-8","volume":"34","author":"DVS Green","year":"2020","unstructured":"Green DVS, Pickett S, Luscombe C, Senger S, Marcus D, Meslamani J, Brett D, Powell A, Masson J (2020) BRADSHAW: a system for automated molecular design. J Comput Aided Mol Design 34(7):747\u2013765. https:\/\/doi.org\/10.1007\/s10822-019-00234-8","journal-title":"J Comput Aided Mol Design"},{"issue":"20","key":"598_CR76","doi-asserted-by":"publisher","first-page":"11964","DOI":"10.1021\/acs.jmedchem.0c01148","volume":"63","author":"JT Bush","year":"2020","unstructured":"Bush JT, Pogany P, Pickett SD, Barker M, Baxter A, Campos S, Cooper AWJ, Hirst D, Inglis G, Nadin A, Patel VK, Poole D, Pritchard J, Washio Y, White G, Green DVS (2020) A turing test for molecular generators. J Med Chem 63(20):11964\u201311971. https:\/\/doi.org\/10.1021\/acs.jmedchem.0c01148","journal-title":"J Med Chem"},{"issue":"12","key":"598_CR77","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1021\/acscentsci.7b00355","volume":"3","author":"CW Coley","year":"2017","unstructured":"Coley CW, Rogers L, Green WH, Jensen KF (2017) Computer-assisted retrosynthesis based on molecular similarity. ACS Cent Sci 3(12):1237\u20131245. https:\/\/doi.org\/10.1021\/acscentsci.7b00355","journal-title":"ACS Cent Sci"},{"issue":"7698","key":"598_CR78","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/nature25978","volume":"555","author":"MHS Segler","year":"2018","unstructured":"Segler MHS, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698):604\u2013610. https:\/\/doi.org\/10.1038\/nature25978","journal-title":"Nature"},{"issue":"1","key":"598_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00472-1","volume":"12","author":"S Genheden","year":"2020","unstructured":"Genheden S, Thakkar A, Chadimov\u00e1 V, Reymond JL, Engkvist O, Bjerrum E (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J Cheminform 12(1):1\u20139. https:\/\/doi.org\/10.1186\/s13321-020-00472-1","journal-title":"J Cheminform"},{"key":"598_CR80","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3389\/fchem.2020.00246","volume":"8","author":"CN Cavasotto","year":"2020","unstructured":"Cavasotto CN, Aucar MG (2020) High-throughput docking using quantum mechanical scoring. Front Chem 8:246. https:\/\/doi.org\/10.3389\/fchem.2020.00246","journal-title":"Front Chem"},{"issue":"4","key":"598_CR81","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1021\/acs.jcim.0c00057","volume":"60","author":"H Guterres","year":"2020","unstructured":"Guterres H, Im W (2020) Improving protein-ligand docking results with high-throughput molecular dynamics simulations. J Chem Inform Model 60(4):2189\u20132198. https:\/\/doi.org\/10.1021\/acs.jcim.0c00057","journal-title":"J Chem Inform Model"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00598-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-022-00598-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00598-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T09:59:11Z","timestamp":1648807151000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-022-00598-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["598"],"URL":"https:\/\/doi.org\/10.1186\/s13321-022-00598-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2021-tdg7f","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]},"assertion":[{"value":"13 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"19"}}