scholarly journals Flame: an open source framework for model development, hosting, and usage in production environments

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Manuel Pastor ◽  
José Carlos Gómez-Tamayo ◽  
Ferran Sanz
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Predictive Models ◽  
Web Application ◽  
Model Building ◽  
Molecular Descriptor ◽  
Model Development ◽  
Graphic Interface ◽  
Chemical Structures ◽  
Production Environments

AbstractThis article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

scholarly journals Flame: An Open Source Framework for Model Development, Hosting, and Usage in Production Environments

2020 ◽  
Author(s):  
Manuel Pastor ◽  
José Carlos Gómez-Tamayo ◽  
Ferran Sanz
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Predictive Models ◽  
Web Application ◽  
Model Building ◽  
Molecular Descriptor ◽  
Model Development ◽  
Graphic Interface ◽  
Chemical Structures ◽  
Production Environments

Abstract This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application, with a Python backend and a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures, since the software supports structural normalization, molecular descriptor generation and machine learning building, using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code adding unlimited model customization. Models generated with Flame can be easily exported facilitating collaborative model development. All models are stored in a persistent model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly three years of development in collaboration with users from pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high quality predictive models based on shared legacy data for assessing the safety of drug candidates.

scholarly journals REDIAL-2020: A Suite of Machine Learning Models to Estimate Anti-SARS-CoV-2 Activities

2020 ◽  
Author(s):  
Govinda KC ◽  
Giovanni Bocci ◽  
Srijan Verma ◽  
Mahmudulla Hassan ◽  
Jayme Holmes ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Web Application ◽  
External Validation ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Virus Infectivity ◽  
Learning Models ◽  
Live Virus ◽  
Machine Learning Models

<p>Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The “best models” are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (<a href="https://drugdiscovery.utep.edu/redial">http://drugcentral.org/Redial</a>). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (<i>viral entry</i>, <i>viral replication,</i> and <i>live virus infectivity</i>) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (<a href="https://github.com/sirimullalab/ncats_covid">https://github.com/sirimullalab/</a>redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.</p>

scholarly journals Lignin Biorefinery Optimization Through Machine Learning

2021 ◽  
Author(s):  
Joakim Löfgren ◽  
Dmitry Tarasov ◽  
Taru Koitto ◽  
Patrick Rinke ◽  
Mikhail Balakshin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Model Building ◽  
Paper Industry ◽  
2D Nmr ◽  
Hydrothermal Pretreatment ◽  
Bayesian Optimization ◽  
Learning Framework ◽  
Point Analysis ◽  
Data Points

Lignin is an abundant biomaterial that currently emerges as a low value by-product in the pulp and paper industry but could be repurposed for high-value products as part of the ongoing global transition to a sustainable society. To increase lignins value, rational and efficient approaches to optimizing lignin biorefineries to produce high value bioproducts are required. Here, we report the optimization of the AquaSolv Omni (AqSO) Biorefinery, a newly introduced biorefinery concept based on hydrothermal pretreatment and solvent extraction. We employ a machine-learning framework based on Bayesian optimization, to provide sample-efficient and guided data collection as well as surrogate model building. The surrogate models allow us to map multiple experimental outputs, including the extracted lignin yield and main structural properties obtained by 2D NMR, as functions of the hydrothermal pretreatment reaction severity and temperature. Our results show that with Bayesian optimization, predictive models can be converged with only 21 data points to within a margin of error comparable to the underlying experimental error. By applying a Pareto point analysis, we demonstrate how the predictive models can be used in tandem to identify optimal extraction conditions for concrete applications in lignin valorization.

scholarly journals Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhi-Min Zou ◽  
De-Hua Chang ◽  
Hui Liu ◽  
Yu-Dong Xiao
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Predictive Models ◽  
Critical Role ◽  
Model Development ◽  
Human Learning ◽  
Therapeutic Outcome ◽  
Treatment Modalities ◽  
Hidden Patterns ◽  
Prognostic Prediction

AbstractWith the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.

scholarly journals REDIAL-2020: A Suite of Machine Learning Models to Estimate Anti-SARS-CoV-2 Activities

2020 ◽  
Author(s):  
Govinda KC ◽  
Giovanni Bocci ◽  
Srijan Verma ◽  
Mahmudulla Hassan ◽  
Jayme Holmes ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Web Application ◽  
External Validation ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Virus Infectivity ◽  
Learning Models ◽  
Live Virus ◽  
Machine Learning Models

<p>Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The “best models” are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (<a href="https://drugdiscovery.utep.edu/redial">http://drugcentral.org/Redial</a>). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (<i>viral entry</i>, <i>viral replication,</i> and <i>live virus infectivity</i>) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (<a href="https://github.com/sirimullalab/ncats_covid">https://github.com/sirimullalab/</a>redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.</p>

scholarly journals REDIAL-2020: A Suite of Machine Learning Models to Estimate Anti-SARS-CoV-2 Activities

2020 ◽  
Author(s):  
Govinda KC ◽  
Giovanni Bocci ◽  
Srijan Verma ◽  
Mahmudulla Hassan ◽  
Jayme Holmes ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Web Application ◽  
External Validation ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Virus Infectivity ◽  
Learning Models ◽  
Live Virus ◽  
Machine Learning Models

<p>Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The “best models” are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (<a href="https://drugdiscovery.utep.edu/redial">http://drugcentral.org/Redial</a>). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (<i>viral entry</i>, <i>viral replication,</i> and <i>live virus infectivity</i>) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (<a href="https://github.com/sirimullalab/ncats_covid">https://github.com/sirimullalab/</a>redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.</p>

scholarly journals Open-source QSAR models for pKa prediction using multiple machine learning approaches

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kamel Mansouri ◽  
Neal F. Cariello ◽  
Alexandru Korotcov ◽  
Valery Tkachenko ◽  
Chris M. Grulke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Acid Dissociation ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Pka Prediction ◽  
Chemical Structures ◽  
Extreme Gradient Boosting ◽  
Qsar Models

Abstract Background The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. Methods The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure–activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). Results The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. Conclusions This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

scholarly journals A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour in C. elegans

2021 ◽  
Author(s):  
Aayushi Vishnoi ◽  
Rati Sharma
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Molecular Descriptor ◽  
Binary Classification ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
C Elegans ◽  
Chemical Structures ◽  
Olfactory Behaviour

The chemical basis of smell remains an unsolved problem, with ongoing studies mapping perceptual descriptor data from human participants to the chemical structures using computational methods. These approaches are, however, limited by linguistic capabilities and inter-individual differences in participants. We use olfactory behaviour data from the nematode C. elegans, which has isogenic populations in a laboratory setting, and employ machine learning approaches for a binary classification task predicting whether or not the worm will be attracted to a given monomolecular odorant. Among others, we use architectures based on Natural Language Processing methods on the SMILES representation of chemicals for molecular descriptor generation and show that machine learning algorithms trained on the descriptors give robust prediction results. We further show, by data augmentation, that increasing the number of samples increases the accuracy of the models. From this detailed analysis, we are able to achieve accuracies comparable to that in human studies and infer that there exists a non trivial relationship between the features of chemical structures and the nematode's behaviour.

scholarly journals REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities

2020 ◽  
Author(s):  
Govinda KC ◽  
Giovanni Bocci ◽  
Srijan Verma ◽  
Md Hassan ◽  
Jayme Holmes ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Web Application ◽  
External Validation ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Virus Infectivity ◽  
Learning Models ◽  
Live Virus ◽  
Machine Learning Models

Abstract Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The “best models” are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.

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