Is Multitask Deep Learning Practical for Pharma?

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery

Comparison of the resistance phenotypes displayed by transformants of the SDHB or SDHC subunits.

<p>Two types of transformants were created, (i) Tr strains where the genes encoding either SDHB or SDHC subunits were ectopically inserted under the control of a GPDA promoter, (ii) HR strains where the WT gene was replaced by a mutated version in its original genomic context. A: SDH inhibition displayed by mitochondrial extracts as measured with the succinate: Q₀/DCPIP reduction test in the presence of varying concentrations of Boscalid. Fitted curve is monophasic with the ectopic transformant containing the WT SDHB expression cassette (black dots) and with the homologous recombinant strain carrying the SDHB_H267L mutation (HR B_H267L, black triangles). Inhibition is biphasic with the ectopic transformant containing the SDHB_H267L expression cassette (Tr B_H267L, black squares). B: Southern blot of genomic DNA extracted from (left to right), the WT (IPO323), one ectopic transformant carrying the WT_SDHB expression cassette, one ectopic transformant carrying the SDHB_H267L expression cassette and signal obtained with one SDHB_H267L homologous recombinant. Boscalid (C) and Fluopyram (D) resistance phenotypes displayed by ectopic and homologous recombinants transformants. In both cases when additional WT SDHB (Tr_WT_SDHB) and SDHC (Tr_WT_SDHC) were inserted ectopically, no significant increase in resistance was observed. When an additional mutated copy of SDHB (Tr_SDHB_H267L) or of SDHC (Tr_SDHC_A84V) was inserted ectopically, significant resistance to the compounds was observed, clearly indicating a dominant effect of the mutated alleles. However, in the homologous recombinant strains where only the mutated subunit SDHB (HR_SDHB_H267L) or SDHC (HR_SDHC_A84_V) was present a further increase in resistance is observed. Whiskers represent minimum and maximum RFs obtained, boxes represent 95% confidence interval and bars value of the median. The data presented correspond to average values of duplicated tests with four independent events of each kind.</p

Tridimensional model of the <i>M. graminicola</i> SDH with Qp site docked carboxamides and interactions to substituted residues.

<p>A: Superposition of all complex II crystal structures with a resolution higher than 3 Å. Only amino acids that are in a 5 Å radius to the bound ligands in the quinone binding site are shown. Amino acids and the heme are represented in lines, the ligands in sticks and water molecules as non bonded spheres. The color code is presented according to atom types. Amino acid and heme carbons are colored in green, ligands carbons in salmon. B: putative binding mode of carboxin in a tridimensional model of <i>M.graminicola</i> SDH. The heme carbons are represented in cyan sticks, the carboxin carbon atoms in salmon. Amino acids that are involved in resistance after mutation are colored in dark blue, amino acids that make key interactions are shown in green and amino acids that are in close proximity to the ligand but which were not found substituted in this study are colored in grey. Hydrogen bonds are shown as yellow dotted lines. C: Putative binding mode of Boscalid in <i>M.graminicola</i> SDH. D: Putative binding mode of Fluopyram in <i>M. graminicola</i> SDH. E: Putative binding mode of Isopyrazam in <i>M. graminicola</i> SDH. F: Model of <i>M. graminicola</i> SDH where SDHB histidine 267 is mutated into a tyrosine. The putative binding mode of the mutated enzyme with Carboxin is shown.</p

Scatter plot presenting <i>in vivo</i> LogIC₅₀ (nM) versus <i>in vitro</i> LogIC₅₀ (nM) adjusted (B) or not (A) for enzyme efficiency.

<p>All data extracted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035429#pone-0035429-t003" target="_blank">table 3</a> and adjusted for amount of enzyme used in the sensitivity test in panel B. Adjustment was performed using enzyme efficiency data extracted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035429#pone-0035429-t004" target="_blank">table 4</a> following a simple equation: if percent efficiency is denoted by E then the efficiency adjusted IC₅₀ = observed IC₅₀×E/100. Following this adjustment the correlation was improved for all compounds.</p

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries or resources to conduct high-throughput screens. Machine learning models have been long established to be more robust and have a larger domain of applicability with larger training sets. Screens over multiple data sets to find compounds with potential malaria blood stage inhibitory activity have been used to generate multiple Bayesian models. Here we describe a method by which Bayesian quantitative structure–activity relationship models, which contain information on thousands to millions of proprietary compounds, can be shared between collaborators at both for-profit and not-for-profit institutions. This model-sharing paradigm allows for the development of consensus models that have increased predictive power over any single model and yet does not reveal the identity of any compounds in the training sets

Localization of substituted amino acids in the SDH subunits of <i>M. graminicola</i> UV mutants and degree of conservation across species.

<p>Asterix indicate substituted residues, conserved residues are shaded in black, dark grey and light grey corresponding to 100%, 80% and 60% conservation respectively. Sequences are from <i>M. graminicola</i> (Mg), <i>Alternaria alternata</i> (Aa), <i>Alternaria oryzae</i> (Ao), <i>B. cinerea</i> (Bc), <i>Magnaporthe grisea</i> (Mag), <i>S. cerevisiae</i> (Sc), <i>Ustilago maydis</i> (Um), <i>G. gallus</i> (Gg), <i>S. scrofa</i> (Ss) and <i>E. coli</i> (Ec).</p

Absence of a major oxidative stress related fitness penalty in <i>M. graminicola</i> homologous recombinant strains carrying Qp site mutations.

<p>A: Respiratory growth of wild type and mutants in the presence of oxidative stresses. Upper panel, AE agar plate growth, middle panel: AE agar supplemented with 10 mM Paraquat, lower panel: AE agar plates were placed in a constant 100% atmosphere. All plates were incubated in same room at room temperature for 6 days. B: Sensitivity towards hydrogen peroxide in liquid AE media. Values presented are the averages from replicates within 3 individual experiments. C: Evaluation of mitochondrial ROS produced <i>in vivo</i> as determined with MitoSOX™ Red fluorescent indicator. Evaluations based on values from over 3 replicates within 3 individual experiments. +PQ corresponds to 1 mM Paraquat supplementation during the incubation period, +H₂O₂ correspond to 10 mM H₂O₂ supplementation during the incubation period. WT (IPO323), and homologous recombinant strains HRAV: SDHC_A84V, HRDG: SDHD_D129G, HRHL: SDHB_H267L, HRHR: SDHC_H152R, HRHY: SDHB_H267Y, HRIV: SDHB_I269V, HRNK: SDHC_N86K, HRRP: SDHB_R265P, HRSG: SDHC_S83G.</p

Mutagenesis experiments overview.

<p>Mutagenesis experiments overview.</p

<i>In vivo</i> and <i>in vitro</i> IC50s and resistance factors overview.

<p>Upper panel: <i>In vivo</i> and <i>in vitro</i> IC50 values obtained for the WT (IPO323) ±S.E. (triplicate). Lower panel: resistance factors (RFs) based on IC50 assessment for a selected subset of representative strains. Presented values are based on the ratio of the means of three individual determinations for the <i>in vivo</i> values and based on the ratio of a single determination for the <i>in vitro</i> values. Presented <i>in vitro</i> values were obtained by calibrating mitochondrial dilutions to obtain similar initial velocity (see<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035429#s4" target="_blank"> material and methods</a>). Cbx strains were originally obtained on Carboxin selection media, Flu on Fluopyram, Bos on Boscalid, Izm on Isopyrazam, Ol on pyrrole compound A. nd*: IC₅₀ could not be determined because fitted curves were not tending to 100% inhibition at infinite AI concentration. FR** (full resistance), no sufficient inhibition detected at highest inhibitor concentration tested.</p

Chemical structure of carboxamides used in the study, trivial and IUPAC denominations.

<p>The generic structure of carboxamides can be divided into 3 main parts. (i) the “acid core” which differs from its ring structure: oxathiin (Carboxin), pyrazole (Isopyrazam), pyrrole (compound A), pyridine (Boscalid), substituted benzene (Fluopyram) (ii) the “linker” which is composed by a benzene ring or a 2-carbon aliphatic spacer in Fluopyram, (iii) the bulky hydrophobic rest, missing in Carboxin.The aim of the figure legend should be to describe the key messages of the figure, but the figure should also be discussed in the text. An enlarged version of the figure and its full legend will often be viewed in a separate window online, and it should be possible for a reader to understand the figure without switching back and forth between this window and the relevant parts of the text. Each legend should have a concise title of no more than 15 words. The legend itself should be succinct, while still explaining all symbols and abbreviations. Avoid lengthy descriptions of methods.</p