scholarly journals An integrative machine learning approach for prediction of toxicity-related drug safety

2018 ◽  
Vol 1 (6) ◽  
pp. e201800098 ◽  
Author(s):  
Artem Lysenko ◽  
Alok Sharma ◽  
Keith A Boroevich ◽  
Tatsuhiko Tsunoda
Keyword(s):  
Machine Learning ◽  
Drug Toxicity ◽  
Drug Targets ◽  
Drug Approval ◽  
Machine Learning Approach ◽  
Related Drug ◽  
Low Toxicity ◽  
Recent Trends ◽  
Toxic Drugs ◽  
Target Sets

Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.

scholarly journals An integrative machine learning approach for prediction of toxicity-related drug safety

2018 ◽  
Author(s):  
Artem Lysenko ◽  
Alok Sharma ◽  
Keith A Boroevich ◽  
Tatsuhiko Tsunoda
Keyword(s):  
Machine Learning ◽  
Drug Toxicity ◽  
Drug Targets ◽  
Biological Network ◽  
Drug Approval ◽  
Biological Network Analysis ◽  
Drug Clinical Trial ◽  
Machine Learning Approach ◽  
Toxicity Risk ◽  
Related Drug

AbstractRecent trends in drug development have been marked by diminishing returns of escalating costs and falling rate of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials as well as the leading cause of drug withdraws after release to market. Computational methods capable of predicting these failures can reduce waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.Summary blurb:Prediction of toxicity-related drug clinical trial failures, withdrawals from market and idiosyncratic toxicity risk by combining biological network analysis with machine learning.

scholarly journals Machine learning prediction of oncology drug targets based on protein and network properties

2019 ◽  
Author(s):  
Zoltan Dezso ◽  
Michele Ceccarelli
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Drug Target ◽  
Drug Targets ◽  
Validation Dataset ◽  
Learning Approach ◽  
Biological Functions ◽  
Machine Learning Approach ◽  
Network Properties ◽  
Trial Drug

Abstract Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an AUC of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

scholarly journals Machine learning prediction of oncology drug targets based on protein and network properties

2019 ◽  
Author(s):  
Zoltan Dezso ◽  
Michele Ceccarelli
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Drug Target ◽  
Drug Targets ◽  
Validation Dataset ◽  
Learning Approach ◽  
Biological Functions ◽  
Machine Learning Approach ◽  
Network Properties ◽  
Trial Drug

Abstract Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an AUC of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

scholarly journals Prediction of ligand-receptor pharmacological activities using a combined docking and machine learning approach

2021 ◽  
Author(s):  
Mireia Jimenez-Roses ◽  
Bradley A Morgan ◽  
Maria Jimenez Sigstad ◽  
T.D. Zoe Tran ◽  
Rohini Srivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Targets ◽  
Binding Pocket ◽  
G Protein Coupled Receptors ◽  
Antagonist Activity ◽  
Pharmacological Activities ◽  
Large Dataset ◽  
Machine Learning Approach ◽  
Receptor Interactions ◽  
G Protein Coupled

G protein coupled receptors (GPCRs) form one of the largest families of proteins in humans, and are valuable therapeutic targets for a variety of different diseases. One central question of drug discovery surrounding GPCRs is what determines the agonism or antagonism exhibited by ligands which bind these important targets. Ligands exert their action via the interactions they make in the ligand binding pocket. We hypothesised that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action. We reasoned that among a large dataset of different ligands, the functionally important interactions will be over-represented. To investigate this hypothesis, we assembled a database of ~2700 known β2AR ligands and computationally docked them to multiple experimentally determined β2AR structures, generating ca 75,000 docking poses. For each docking pose, we predicted all interactions between the atoms of the receptor and the atoms of the ligand. Using Machine Learning (ML) we identified specific interactions that correlated with the agonist or antagonist activity of these ligands, and developed ML-based predictors of agonist/antagonist activity with up to 90% accuracy. This approach can be readily applied to other GPCRs and drug targets beyond GPCRs.

scholarly journals Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

2019 ◽  
Author(s):  
Robert Ietswaart ◽  
Seda Arat ◽  
Amanda X. Chen ◽  
Saman Farahmand ◽  
Bumjun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adverse Drug Reactions ◽  
Drug Targets ◽  
Bile Acid Metabolism ◽  
Drug Reactions ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models ◽  
Model Features

AbstractAdverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines. Here, we analyze in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles. By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Among these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs. These associations provide a comprehensive resource to support drug development and human biology studies.

scholarly journals A machine learning approach predicts essential genes and pharmacological targets in cancer

2019 ◽  
Author(s):  
Coryandar Gilvary ◽  
Neel S. Madhukar ◽  
Kaitlyn Gayvert ◽  
Miguel Foronda ◽  
Alexendar Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Targets ◽  
Essential Genes ◽  
Learning Approach ◽  
Loss Of Function ◽  
Pharmacological Targets ◽  
Recent Emergence ◽  
Machine Learning Approach ◽  
Drug Treatments ◽  
Potential Cancer

ABSTRACTLoss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

scholarly journals Prediction of drug targets for specific diseases leveraging gene perturbation data: A machine learning approach

2021 ◽  
Author(s):  
Kai Zhao ◽  
Yujia Shi ◽  
Hon-Cheong SO
Keyword(s):  
Machine Learning ◽  
Drug Targets ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Drug Induced ◽  
Over Expression ◽  
Machine Learning Approach ◽  
Perturbation Data ◽  
Significant Enrichment ◽  
Gene Perturbations

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational target discovery approach based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles, with outcomes defined as whether the drug treats the studied disease. The goal is to learn expression patterns associated with treatment. The fitted ML models were then applied to expression profiles from gene perturbations (over-expression[OE]/knockdown[KD]). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA) and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may re-discover known drug targets from an external database (OpenTargets). We indeed found evidence of significant enrichment across all diseases under study. Further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with treatment of RA, which was supported by a study showing PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing targets.

scholarly journals Machine learning prediction of oncology drug targets based on protein and network properties

2020 ◽  
Author(s):  
Zoltan Dezso ◽  
Michele Ceccarelli
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Drug Target ◽  
Drug Targets ◽  
Validation Dataset ◽  
Learning Approach ◽  
Biological Functions ◽  
Machine Learning Approach ◽  
Network Properties ◽  
Trial Drug

Abstract Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an AUC of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

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