Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386158059> ?p ?o ?g. }
- W4386158059 endingPage "5528" @default.
- W4386158059 startingPage "5513" @default.
- W4386158059 abstract "Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM." @default.
- W4386158059 created "2023-08-26" @default.
- W4386158059 creator A5007900033 @default.
- W4386158059 creator A5021052785 @default.
- W4386158059 creator A5024496300 @default.
- W4386158059 creator A5029003935 @default.
- W4386158059 creator A5058139786 @default.
- W4386158059 creator A5061084701 @default.
- W4386158059 creator A5069015094 @default.
- W4386158059 creator A5075210236 @default.
- W4386158059 creator A5083226872 @default.
- W4386158059 creator A5083286341 @default.
- W4386158059 date "2023-08-25" @default.
- W4386158059 modified "2023-10-17" @default.
- W4386158059 title "Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries" @default.
- W4386158059 cites W1678356000 @default.
- W4386158059 cites W1984994707 @default.
- W4386158059 cites W1988037271 @default.
- W4386158059 cites W1988111902 @default.
- W4386158059 cites W1990451437 @default.
- W4386158059 cites W1991001144 @default.
- W4386158059 cites W2017398555 @default.
- W4386158059 cites W2031390939 @default.
- W4386158059 cites W2033757486 @default.
- W4386158059 cites W2043509228 @default.
- W4386158059 cites W2045722951 @default.
- W4386158059 cites W2046128919 @default.
- W4386158059 cites W2046344452 @default.
- W4386158059 cites W2053717624 @default.
- W4386158059 cites W2056881083 @default.
- W4386158059 cites W2093426296 @default.
- W4386158059 cites W2100333708 @default.
- W4386158059 cites W2120240539 @default.
- W4386158059 cites W2128879247 @default.
- W4386158059 cites W2148317584 @default.
- W4386158059 cites W2171830166 @default.
- W4386158059 cites W2290847742 @default.
- W4386158059 cites W2484065175 @default.
- W4386158059 cites W2556851635 @default.
- W4386158059 cites W2567183420 @default.
- W4386158059 cites W2621020942 @default.
- W4386158059 cites W2735246657 @default.
- W4386158059 cites W2771417816 @default.
- W4386158059 cites W2771732756 @default.
- W4386158059 cites W2806547269 @default.
- W4386158059 cites W2810711993 @default.
- W4386158059 cites W28412257 @default.
- W4386158059 cites W2889555425 @default.
- W4386158059 cites W2899587428 @default.
- W4386158059 cites W2911964244 @default.
- W4386158059 cites W2912171584 @default.
- W4386158059 cites W2920995682 @default.
- W4386158059 cites W2921262115 @default.
- W4386158059 cites W2940242941 @default.
- W4386158059 cites W2949923262 @default.
- W4386158059 cites W2981514797 @default.
- W4386158059 cites W2983225234 @default.
- W4386158059 cites W3004731690 @default.
- W4386158059 cites W3004732066 @default.
- W4386158059 cites W3007309629 @default.
- W4386158059 cites W3010016408 @default.
- W4386158059 cites W3012320348 @default.
- W4386158059 cites W3023042104 @default.
- W4386158059 cites W3034612930 @default.
- W4386158059 cites W3043660595 @default.
- W4386158059 cites W3094640617 @default.
- W4386158059 cites W3102476541 @default.
- W4386158059 cites W3104508774 @default.
- W4386158059 cites W3108778136 @default.
- W4386158059 cites W3109087942 @default.
- W4386158059 cites W3112474878 @default.
- W4386158059 cites W3113150977 @default.
- W4386158059 cites W3126785626 @default.
- W4386158059 cites W3138526880 @default.
- W4386158059 cites W3193127957 @default.
- W4386158059 cites W4200233821 @default.
- W4386158059 cites W4212883601 @default.
- W4386158059 cites W4223920159 @default.
- W4386158059 cites W4254278957 @default.
- W4386158059 cites W4307722378 @default.
- W4386158059 cites W4309923193 @default.
- W4386158059 cites W4310603653 @default.
- W4386158059 cites W4313703134 @default.
- W4386158059 cites W4320857994 @default.
- W4386158059 cites W4367049415 @default.
- W4386158059 cites W4378212018 @default.
- W4386158059 cites W58105655 @default.
- W4386158059 doi "https://doi.org/10.1021/acs.jcim.3c00912" @default.
- W4386158059 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37625010" @default.
- W4386158059 hasPublicationYear "2023" @default.
- W4386158059 type Work @default.
- W4386158059 citedByCount "0" @default.
- W4386158059 crossrefType "journal-article" @default.
- W4386158059 hasAuthorship W4386158059A5007900033 @default.
- W4386158059 hasAuthorship W4386158059A5021052785 @default.
- W4386158059 hasAuthorship W4386158059A5024496300 @default.
- W4386158059 hasAuthorship W4386158059A5029003935 @default.
- W4386158059 hasAuthorship W4386158059A5058139786 @default.