ChEMBL is a large, open-access bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012, 2014 and 2017 Nucleic Acids Research Database Issues. In the last two years, several important improvements have been made to the database and are described here. These include more robust capture and representation of assay details; a new data deposition system, allowing updating of data sets and deposition of supplementary data; and a completely redesigned web interface, with enhanced search and filtering capabilities.

36Low success rates during drug development are due in part to the difficulty of 37 defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, 38 we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs 39 and genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically 40 investigate in cellular drug mechanism-of-action. We observed an enrichment for 41 positive associations between drug sensitivity and knockout of their nominal targets, 42 and by leveraging protein-protein networks we identified pathways that mediate drug 43 response. This revealed an unappreciated role of mitochondrial E3 ubiquitin-protein 44 ligase MARCH5 in sensitivity to MCL1 inhibitors. We also estimated drug on-target and 45 off-target activity, informing on specificity, potency and toxicity. Linking drug and gene 46 dependency together with genomic datasets uncovered contexts in which molecular 47 networks when perturbed mediate cancer cell loss-of-fitness, and thereby provide 48 independent and orthogonal evidence of biomarkers for drug development. This study 49 illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function 50 screens can elucidate mechanism-of-action to advance drug development. 51 inhibitors ( Supplementary Figure 1d and1e). 126 127 Cell fitness effects for 16,643 gene knockouts have been measured using genome-128 wide CRISPR-Cas9 screens at the Sanger and Broad Institutes (Meyers et al, 2017; Behan et 129 al, 2019; DepMap, 2019) (Supplementary Table 4). The first PC across the cell lines (6.8% 130 variance explained) separated the two institutes of origin ( Supplementary Figure 2a), 131 consistent with a comparative analysis performed on an overlapping set of cell lines (Dempster 132 et al, 2019). Growth rate was less significantly associated with CRISPR knockout response 133 (Supplementary Figure 2b and 2c). 134 157 are MCL1 and BCL2 selective inhibitors, respectively. Gene fitness log2 fold-changes (FC) are scaled by using 158 Supplementary Figure 2. Overview of the CRISPR-Cas9 datasets. a, PCA analysis of the samples in the 616 CRISPR-Cas9 screens, samples institute of origin is highlighted. b, correlation coefficients between all top 10 PCs 617 and growth rate. c, correlation between cell lines growth rate and PC3 (Pearson correlation coefficient reported in 618 the top left). 619 23 620 Supplementary Figure 3. Drug response and gene fitness associations. a, total number of drugs utilised in the 621 study and the different levels of information available: 'All' represents all the drugs including replicates screened 622 with different technologies (GDSC1 and GDSC2); 'Unique' counts the number of unique drug names; 'Annotated' 623 shows the number of unique drugs with manual annotation of nominal targets; and 'Target tested' represents the 624 number of unique drugs, with target information, for which the target has been knocked-out in the CRISPR-Cas9 625 screens. b, histogram of the drug-gene associations effect sizes ...

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitinprotein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug ontarget and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

Cattle temperament is a complex trait, and molecular studies aimed at defining this trait are scarce. We used an interaction networks approach to identify new genes (interacting genes) and to estimate their effects and those of 19 dopamine- and serotonin-related genes on the temperament traits of Charolais cattle. The genes proopiomelanocortin (POMC), neuropeptide Y (NPY), solute carrier family 18, member 2 (SLC18A2) and FBJ murine osteosarcoma viral oncogene homologue (FOSFBJ) were identified as new candidates. Their potential to be associated with temperament was estimated according to their reported biological activities, which included interactions with neural activity, receptor function, targeting or synthesis of neurotransmitters and association with behaviour. Pen score (PS) and exit velocity (EV) measures were determined from 412 Charolais cows to calculate their temperament score (TS). Based on the TS, calm (n = 55; TS, 1.09 ± 0.33) and temperamental (n = 58; TS, 2.27 ± 0.639) cows were selected and genotyped using a 248 single-nucleotide variation (SNV) panel. Of the 248 variations in the panel, only 151 were confirmed to be polymorphic (single-nucleotide polymorphisms; SNPs) in the tested population. Single-marker association analyses between genotypes and temperament measures (EV, PS and/or TS) indicated significant associations of six SNPs from four candidate genes. The markers rs109576799 and rs43696138, located in the DRD3 and HTR2A genes, respectively, were significantly associated with both EV and TS traits. Four markers, rs110365063 and rs137756569 from the POMC gene and rs110365063 and rs135155082 located in SLC18A2 and DRD2, respectively, were associated with PS. The variant rs110365063 located in bovine SLC18A2 causes a change in the amino acid sequence from Ala to Thr. Further studies are needed to confirm the association of genetic profile with cattle temperament; however, our study represents important progress in understanding the regulation of cattle temperament by different genes with divergent functions.

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