AIMC Topic: Drug Discovery
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Systematic Modeling of log Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.
Journal of chemical information and modeling
Jan 10, 2020
Lipophilicity, as evaluated by the -octanol/buffer solution distribution coefficient at pH = 7.4 (log ), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In thi...
Machine and deep learning approaches for cancer drug repurposing.
Seminars in cancer biology
Jan 3, 2020
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targ...
Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.
Nature reviews. Gastroenterology & hepatology
Jan 3, 2020
Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted thera...
Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques.
Computer methods and programs in biomedicine
Dec 27, 2019
BACKGROUND: The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of anti-tuberculosis drug-induced hepatotoxicity.
Support Vector Machine
Liver
Taiwan
Aged
ROC Curve
Polymorphism, Genetic
Genomics
Aged, 80 and over
Chemical and Drug Induced Liver Injury
Female
Humans
Genotype
Adult
Neural Networks, Computer
Middle Aged
Antitubercular Agents
Reproducibility of Results
Sensitivity and Specificity
Machine Learning
Drug Discovery
Risk Factors
Tuberculosis
Male
Gene Frequency
Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces.
BMC bioinformatics
Dec 27, 2019
BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interacti...
DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.
Journal of chemical information and modeling
Dec 20, 2019
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a s...
iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.
International journal of molecular sciences
Dec 20, 2019
Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the p...
Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network.
Molecules (Basel, Switzerland)
Dec 15, 2019
Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purific...
A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.
International journal of molecular sciences
Dec 12, 2019
Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecu...
The NAD-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications.
Mechanisms of ageing and development
Dec 5, 2019
Nicotinamide adenine dinucleotide (NAD) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS, β-oxidation, and is a co-factor for proteins promoting healthy longevity. NAD depletion is associa...
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