Imputation versus prediction: applications in machine learning for drug discovery

Imputation is a powerful statistical method that is distinct from the predictive modelling techniques more commonly used in drug discovery. Imputation uses sparse experimental data in an incomplete dataset to predict missing values by leveraging correlations between experimental assays. This contrasts with quantitative structure–activity relationship methods that use only descriptor – assay correlations. We summarize three recent imputation strategies – heterogeneous deep imputation, assay profile methods and matrix factorization – and compare these with quantitative structure–activity relationship methods, including deep learning, in drug discovery settings. We comment on the value added by imputation methods when used in an ongoing project and find that imputation produces stronger models, earlier in the project, over activity and absorption, distribution, metabolism and elimination end points. </jats:p

Deep imputation on large‐scale drug discovery data

More accurate predictions of the biological properties of chemical compounds would guide the selection and design of new compounds in drug discovery and help to address the enormous cost and low success-rate of pharmaceutical R&D. However this domain presents a significant challenge for AI methods due to the sparsity of compound data and the noise inherent in results from biological experiments. In this paper, we demonstrate how data imputation using deep learning provides substantial improvements over quantitative structure-activity relationship (QSAR) machine learning models that are widely applied in drug discovery. We present the largest-to-date successful application of deep-learning imputation to datasetswhich arecomparablein sizetothe corporate data repository of a pharmaceutical company (678,994 compounds by 1166 endpoints). We demonstrate this improvement for three areas of practical application linked to distinct use cases; i) target activity data compiled from a range of drug discovery projects, ii) a high value and heterogeneous datasetcovering complex absorption, distribution, metabolism and elimination properties and, iii) high throughput screeningdata, testing thealgorithm’slimits on early-stage noisy and very sparse data.Achieving median coefficients of determination, 2, of 0.69, 0.36 and 0.43 respectively across these applications, the deep learning imputation method offers an unambiguous improvement over random forest QSAR methods, which achieve median 2 values of 0.28, 0.19 and 0.23 respectively.We also demonstrate that robust estimates of the uncertainties in the predicted values correlate strongly with the accuracies in prediction, enabling greater confidence in decision-making based on the imputed values.Optibrium Ltd, Intellegens Ltd, Takeda, Royal Societ

GABA receptor associated protein changes the electrostatic environment around the GABA type A receptor.

Funder: Science and Technology Facilities Council; Id: http://dx.doi.org/10.13039/501100000271We have performed fully atomistic molecular dynamics simulations of the intracellular domain of a model of the GABAA receptor with and without the GABA receptor associated protein (GABARAP) bound. We have also calculated the electrostatic potential due to the receptor, in the absence and presence of GABARAP. We find that GABARAP binding changes the electrostatic properties around the GABAA receptor and could lead to increased conductivity of chloride ions through the receptor. We also find that ion motions that would result in conducting currents are observed nearly twice as often when GABARAP binds. These results are consistent with data from electrophysiological experiments

Meta-Alignment with Crumble and Prune: Partitioning very large alignment problems for performance and parallelization

<p>Abstract</p> <p>Background</p> <p>Continuing research into the global multiple sequence alignment problem has resulted in more sophisticated and principled alignment methods. Unfortunately these new algorithms often require large amounts of time and memory to run, making it nearly impossible to run these algorithms on large datasets. As a solution, we present two general methods, Crumble and Prune, for breaking a phylogenetic alignment problem into smaller, more tractable sub-problems. We call Crumble and Prune <it>meta-alignment </it>methods because they use existing alignment algorithms and can be used with many current alignment programs. Crumble breaks long alignment problems into shorter sub-problems. Prune divides the phylogenetic tree into a collection of smaller trees to reduce the number of sequences in each alignment problem. These methods are orthogonal: they can be applied together to provide better scaling in terms of sequence length and in sequence depth. Both methods partition the problem such that many of the sub-problems can be solved independently. The results are then combined to form a solution to the full alignment problem.</p> <p>Results</p> <p>Crumble and Prune each provide a significant performance improvement with little loss of accuracy. In some cases, a gain in accuracy was observed. Crumble and Prune were tested on real and simulated data. Furthermore, we have implemented a system called Job-tree that allows hierarchical sub-problems to be solved in parallel on a compute cluster, significantly shortening the run-time.</p> <p>Conclusions</p> <p>These methods enabled us to solve gigabase alignment problems. These methods could enable a new generation of biologically realistic alignment algorithms to be applied to real world, large scale alignment problems.</p

Influence of oxygen tension on myocardial performance. Evaluation by tissue Doppler imaging

BACKGROUND: Low O(2 )tension dilates coronary arteries and high O(2 )tension is a coronary vasoconstrictor but reports on O(2)-dependent effects on ventricular performance diverge. Yet oxygen supplementation remains first line treatment in cardiovascular disease. We hypothesized that hypoxia improves and hyperoxia worsens myocardial performance. METHODS: Seven male volunteers (mean age 38 ± 3 years) were examined with echocardiography at respiratory equilibrium during: 1) normoxia (≈21% O(2), 79% N(2)), 2) while inhaling a hypoxic gas mixture (≈11% O(2), 89% N(2)), and 3) while inhaling 100% O(2). Tissue Doppler recordings were acquired in the apical 4-chamber, 2-chamber, and long-axis views. Strain rate and tissue tracking displacement analyses were carried out in each segment of the 16-segment left ventricular model and in the basal, middle and apical portions of the right ventricle. RESULTS: Heart rate increased with hypoxia (68 ± 4 bpm at normoxia vs. 79 ± 5 bpm, P < 0.001) and decreased with hyperoxia (59 ± 5 bpm, P < 0.001 vs. normoxia). Hypoxia increased strain rate in four left ventricular segments and global systolic contraction amplitude was increased (normoxia: 9.76 ± 0.41 vs hypoxia: 10.87 ± 0.42, P < 0.001). Tissue tracking displacement was reduced in the right ventricular segments and tricuspid regurgitation increased with hypoxia (7.5 ± 1.9 mmHg vs. 33.5 ± 1.8 mmHg, P < 0.001). The TEI index and E/E' did not change with hypoxia. Hyperoxia reduced strain rate in 10 left ventricular segments, global systolic contraction amplitude was decreased (8.83 ± 0.38, P < 0.001 vs. normoxia) while right ventricular function was unchanged. The spectral and tissue Doppler TEI indexes were significantly increased but E/E' did not change with hyperoxia. CONCLUSION: Hypoxia improves and hyperoxia worsens systolic myocardial performance in healthy male volunteers. Tissue Doppler measures of diastolic function are unaffected by hypoxia/hyperoxia which support that the changes in myocardial performance are secondary to changes in vascular tone. It remains to be settled whether oxygen therapy to patients with heart disease is a consistent rational treatment

Distribution of causes of maternal mortality among different socio-demographic groups in Ghana; a descriptive study

BACKGROUND: Ghana's maternal mortality ratio remains high despite efforts made to meet Millennium Development Goal 5. A number of studies have been conducted on maternal mortality in Ghana; however, little is known about how the causes of maternal mortality are distributed in different socio-demographic subgroups. Therefore the aim of this study was to assess and analyse the causes of maternal mortality according to socio-demographic factors in Ghana.METHODS: The causes of maternal deaths were assessed with respect to age, educational level, rural/urban residence status and marital status. Data from a five year retrospective survey was used. The data was obtained from Ghana Maternal Health Survey 2007 acquired from the database of Ghana Statistical Service. A total of 605 maternal deaths within the age group 12-49 years were analysed using frequency tables, cross-tabulations and logistic regression.RESULTS: Haemorrhage was the highest cause of maternal mortality (22.8%). Married women had a significantly higher risk of dying from haemorrhage, compared with single women (adjusted OR = 2.7, 95%CI = 1.2-5.7). On the contrary, married women showed a significantly reduced risk of dying from abortion compared to single women (adjusted OR = 0.2, 95%CI = 0.1-0.4). Women aged 35-39 years had a significantly higher risk of dying from haemorrhage (aOR 2.6, 95%CI = 1.4-4.9), whereas they were at a lower risk of dying from abortion (aOR 0.3, 95% CI = 0.1-0.7) compared to their younger counterparts. The risk of maternal death from infectious diseases decreased with increasing maternal age, whereas the risk of dying from miscellaneous causes increased with increasing age.CONCLUSIONS: The study shows evidence of variations in the causes of maternal mortality among different socio-demographic subgroups in Ghana that should not be overlooked. It is therefore recommended that interventions aimed at combating the high maternal mortality in Ghana should be both cause-specific as well as target-specific

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others

Antibiotic Prescribing Patterns in Ghana, Uganda, Zambia and Tanzania Hospitals: Results from the Global Point Prevalence Survey (G-PPS) on Antimicrobial Use and Stewardship Interventions Implemented

Antimicrobial resistance (AMR) remains an important global public health issue with antimicrobial misuse and overuse being one of the main drivers. The Global Point Prevalence Survey (G-PPS) of Antimicrobial Consumption and Resistance assesses the prevalence and the quality of antimicrobial prescriptions across hospitals globally. G-PPS was carried out at 17 hospitals across Ghana, Uganda, Zambia and Tanzania. The overall prevalence of antimicrobial use was 50% (30–57%), with most antibiotics prescribed belonging to the WHO ‘Access’ and ‘Watch’ categories. No ‘Reserve’ category of antibiotics was prescribed across the study sites while antimicrobials belonging to the ‘Not Recommended’ group were prescribed infrequently. Antimicrobials were most often prescribed for prophylaxis for obstetric or gynaecological surgery, making up between 12 and 18% of total prescriptions across all countries. The most prescribed therapeutic subgroup of antimicrobials was ‘Antibacterials for systemic use’. As a result of the programme, PPS data are now readily available for the first time in the hospitals, strengthening the global commitment to improved antimicrobial surveillance. Antimicrobial stewardship interventions developed included the formation of AMS committees, the provision of training and the preparation of new AMS guidelines. Other common interventions included the presentation of findings to clinicians for increased awareness, and the promotion of a multi-disciplinary approach to successful AMS programmes. Repeat PPS would be necessary to continually monitor the impact of interventions implemented. Broader participation is also encouraged to strengthen the evidence base

N-glycosylation of mouse TRAIL-R and human TRAIL-R1 enhances TRAIL-induced death.

APO2L/TRAIL (TNF-related apoptosis-inducing ligand) induces death of tumor cells through two agonist receptors, TRAIL-R1 and TRAIL-R2. We demonstrate here that N-linked glycosylation (N-glyc) plays also an important regulatory role for TRAIL-R1-mediated and mouse TRAIL receptor (mTRAIL-R)-mediated apoptosis, but not for TRAIL-R2, which is devoid of N-glycans. Cells expressing N-glyc-defective mutants of TRAIL-R1 and mouse TRAIL-R were less sensitive to TRAIL than their wild-type counterparts. Defective apoptotic signaling by N-glyc-deficient TRAIL receptors was associated with lower TRAIL receptor aggregation and reduced DISC formation, but not with reduced TRAIL-binding affinity. Our results also indicate that TRAIL receptor N-glyc impacts immune evasion strategies. The cytomegalovirus (CMV) UL141 protein, which restricts cell-surface expression of human TRAIL death receptors, binds with significant higher affinity TRAIL-R1 lacking N-glyc, suggesting that this sugar modification may have evolved as a counterstrategy to prevent receptor inhibition by UL141. Altogether our findings demonstrate that N-glyc of TRAIL-R1 promotes TRAIL signaling and restricts virus-mediated inhibition