scholarly journals Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning

MedChemComm ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 1538-1546 ◽  
Author(s):  
Jens-Alexander Fuchs ◽  
Francesca Grisoni ◽  
Michael Kossenjans ◽  
Jan A. Hiss ◽  
Gisbert Schneider
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
In Silico ◽  
Medicinal Chemistry ◽  
Peptide Derivatives ◽  
Lipophilicity Prediction

Lipophilicity prediction is routinely applied to small molecules. For compounds outside the domain of classical medicinal chemistry these predictions lack accuracy, advocating the development of bespoke in silico approaches.

scholarly journals Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small Molecule Inhibitors of Escherichia coli

2021 ◽  
Author(s):  
Sara S. El Zahed ◽  
Shawn French ◽  
Maya A. Farha ◽  
Garima Kumar ◽  
Eric D. Brown
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Antibacterial Activity ◽  
Small Molecules ◽  
Small Molecule ◽  
In Silico ◽  
Molecular Descriptors ◽  
Structural Parameters ◽  
Side Chain ◽  
Gram Negative

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 actives from a small molecule screen in efflux-compromised Escherichia coli. We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli. Further, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico. Here, SERF demonstrated high accuracy in identifying such molecules. Further, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side chain modification to design novel antimicrobials evading efflux pumps.

In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm

2019 ◽  
Vol 63 (12) ◽  
pp. 6499-6512 ◽  
Author(s):  
Suqing Zheng ◽  
Yibing Wang ◽  
Wenxin Liu ◽  
Wenping Chang ◽  
Guang Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Small Molecules ◽  
In Silico ◽  
Human Erythrocytes ◽  
In Silico Prediction ◽  
Hemolytic Toxicity

scholarly journals In silico prediction of chemical subcellular localization via multi-classification methods

MedChemComm ◽  
2017 ◽  
Vol 8 (6) ◽  
pp. 1225-1234 ◽  
Author(s):  
Hongbin Yang ◽  
Xiao Li ◽  
Yingchun Cai ◽  
Qin Wang ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Small Molecules ◽  
In Silico ◽  
Classification Models ◽  
Classification Methods ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multi Classification

Multi-classification models were developed for prediction of subcellular localization of small molecules by machine learning methods.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Medicinal Chemistry Database GDBMedChem

2019 ◽  
Author(s):  
Mahendra Awale ◽  
Finton Sirockin ◽  
Nikolaus Stiefl ◽  
Jean-Louis Reymond
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Medicinal Chemistry ◽  
3D Visualization ◽  
Molecular Size ◽  
Similarity Searching ◽  
Complex Molecules ◽  
Synthetic Accessibility ◽  
Simple Chemical ◽  
Reduced Complexity

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

In the Quest for a Stable Triplet State in Small Polyaromatic Hydrocarbons : An In-Silico Tool for Rational Design and Prediction

2019 ◽  
Author(s):  
Madhumita Rano ◽  
Sumanta K Ghosh ◽  
Debashree Ghosh
Keyword(s):  
Triplet State ◽  
Small Molecules ◽  
In Silico ◽  
Qualitative Agreement ◽  
Rational Design ◽  
Polyaromatic Hydrocarbons ◽  
Spin Hamiltonian ◽  
Spin Frustration ◽  
Computationally Efficient ◽  
High Level

<div>Combining the roles of spin frustration and geometry of odd and even numbered rings in polyaromatic hydrocarbons (PAHs), we design small molecules that show exceedingly small singlet-triplet gaps and stable triplet ground states. Furthermore, a computationally efficient protocol with a model spin Hamiltonian is shown to be capable of qualitative agreement with respect to high level multireference calculations and therefore, can be used for fast molecular discovery and screening.</div>

In Silico Machine Learning Methods in Drug Development

2014 ◽  
Vol 14 (16) ◽  
pp. 1913-1922 ◽  
Author(s):  
Dimitar Dobchev ◽  
Girinath Pillai ◽  
Mati Karelson
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
In Silico ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

In silico prediction of chemical respiratory toxicity via machine learning

2021 ◽  
Vol 18 ◽  
pp. 100155
Author(s):  
Zhiyuan Wang ◽  
Piaopiao Zhao ◽  
Xiaoxiao Zhang ◽  
Xuan Xu ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Respiratory Toxicity
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