New paper: Chandra, Banerjee & Roy (2026) used #alvaDesc descriptors to build ML/QSPR models for predicting log CMC of PFASs and surfactants, identifying key structural drivers and property cliffs.

Paper: pubs.acs.org/doi/full/10....
alvaDesc: alvascience.com/alvadesc/

#QSPR #QSAR #chemsky

🔗 Explore alvaDesc and see how it can support your cheminformatics and QSAR workflows: alvascience.com/alvadesc/

#Cheminformatics #QSPR #RASPR #Antioxidants #FoodChemistry #Nutraceuticals #alvaDesc #Alvascience

New in Food Chemistry (Kumar et al., 2026): a similarity-augmented q-RASPR model predicts antioxidant activity from DPPH data (1911 molecules) and screens ~70,000 food compounds from FooDB using 2D descriptors from alvaDesc.

📄 Paper: doi.org/10.1016/j.fo...

#alvaDesc #QSPR

Development of stacked ensemble models for estimating the physicochemical properties of organic chemicals - Structural Chemistry Structural Chemistry - The physicochemical properties of chemicals are essential for understanding their behaviour within biological systems and their environmental impacts. With the rapid increase...

📢 New Publication | Stacked Ensemble QSPR Models for Physicochemical Properties

Using a large and diverse dataset of 14,207 chemicals and descriptors calculated with #alvaDesc.

🔗 Paper: doi.org/10.1007/s112...
🔗 alvaDesc: alvascience.com/alvadesc/

#QSPR #MachineLearning #chemsky

Molecular Descriptors-Based Analysis for Computation of Polyurethane Melting Temperature - Multiscale Science and Engineering Multiscale Science and Engineering - Polyurethane (PU) is a highly popular polymer used in several industrial spheres, the thermal and mechanical functioning of which is determined most important...

New study using #alvaDesc & #alvaModel to predict polyurethane melting temperature (Tm) via #QSPR
5,600+ descriptors tested; PLS delivered the best performance, supporting fast, cost-effective polymer design.

📖 doi.org/10.1007/s424...
🔗 alvascience.com/alvadesc/

#Polymers #cheminformatics #chemsky

📘 New paper (Ghosh et al., 2026): interpretable ML/QSPR models to predict sweet vs non-sweet and bitter vs non-bitter compounds on a large curated dataset.
Paper: doi.org/10.1016/j.ji...

#QSPR #bitterness #sweetness #moleculardescriptors #alvaDesc #chemsky

📊 Dataset comparison in alvaModel

A short video showing how to compare training/test datasets using box plots and stacked bars to spot distribution shifts and class imbalance, before building QSAR/QSPR models.

🎥 www.youtube.com/watch?v=EMSt...

#QSAR #QSPR #Cheminformatics #alvaModel #chemsky

🚀 New video! Quick intro to alvaModel 3.0 - our tool for building and validating QSAR/QSPR models with an intuitive workflow and OECD compliance.

▶️ youtube.com/watch?v=5TVc...
More info: alvascience.com/alvamodel/

#QSAR #QSPR #Cheminformatics #alvaModel #chemsky

🔗 Read the paper: doi.org/10.1115/1.40...
🔗 Learn more about alvaDesc: alvascience.com/alvadesc/%EF...

#alvaDesc #Cheminformatics #MachineLearning #FuelResearch #Terpenes #Sustainability #QSPR #ANN #RenewableEnergy

🎥 Introduction video: youtu.be/5TVcpxTdQAY
🔗 More info at: alvascience.com/alvamodel

#QSAR #Cheminformatics #DrugDiscovery #alvaModel #AI4Science #QSPR #MachineLearning

New QSPR study in J. Mol. Graphics & Modelling by Ouaissa et al. (2026) used #alvaDesc and #alvaModel to predict logP of psychoanaleptic drugs.

📄 doi.org/10.1016/j.jm...
🔗 alvascience.com

#Cheminformatics #MachineLearning #QSPR #DrugDiscovery

Modeling benzene physicochemical properties using Zagreb upsilon indices Quantitative structure–property relationship (QSPR) frameworks leverage topological indices to model the physicochemical attributes of molecular structures. In this study, we introduce the concept of…

This study introduces Zagreb upsilon indices based on the #upsilon degree, showing that the second Zagreb upsilon index outperforms existing topological indices in predicting π-electron energy of #benzenes (r > 0.93). #QSPR

#OpenAccess in Open Chemistry:

📄 Polymers: Ascencio-Medina et al. (2024) built #QSPR models to predict polymer dielectric constants.

🔑 1,273 descriptors from #alvaDesc, reduced via GA → GBR models outperformed linear methods (R² ~0.94 train, ~0.82 test)

📄 doi.org/10.3390/poly...
🔗 alvascience.com/alvaDesc/

#chemsky

Interested in QSAR/QSPR modeling?
Check out this short video introducing alvaModel, a tool for building and validating predictive models in cheminformatics.

🎥 www.youtube.com/watch?v=U0J2...
🔗 www.alvascience.com/alvamodel/

#QSAR #QSPR #Cheminformatics #alvaModel #MachineLearning #chemsky

📄 Zhu et al. (2025) developed 26 QSPR models to predict adsorption (log K) of organic contaminants using ML and alvaDesc descriptors.

🔗 Paper: doi.org/10.1016/j.ma...
🔗 More on alvaDesc: alvascience.com/alvadesc/

#QSPR #ML #Cheminformatics #moleculardescriptors #chemsky

New QSPR study by Ascencio-Medina et al. explores dielectric constant prediction in polymers using ML and alvaDesc molecular descriptors.
🔗 doi.org/10.3390/poly...

More on alvaDesc: alvascience.com/alvadesc/

#moleculardescriptor #QSPR #Polymers #ML #alvaDesc #Cheminformatics #chemsky

Predictive Modeling of Yield Sooting Index Using Machine Learning with Uncertainty Estimation This study explores the development of two predictive models for the yield sooting index (YSI) of various fuels using the advanced capabilities of machine learning (ML), particularly multilayer percep...

Hosni et al. used alvaDesc to develop ML models predicting Yield Sooting Index (YSI) of hydrocarbons, with uncertainty estimation. A QSPR approach applied to combustion chemistry. doi.org/10.1021/acso...

More on alvaDesc: alvascience.com/alvadesc/

#QSPR #Combustion #SootingIndex #chemsky

Alvascience Tools Now Fully Optimized for Apple Silicon - Alvascience Alvascience Tools Now Fully Optimized for Apple Silicon Apple's M1 processor (Image source: apple.com) With the release of alvaDesc 3.0, all Alvascience tools, including alvaDesc, alvaBuilder, alvaMod...

🚀 All Alvascience tools are fully optimized for Apple Silicon (M1/M2/M3)!

Enjoy faster performance on macOS for molecular descriptors, QSAR/QSPR modeling, and more.

🔗 alvascience.com/alvascience-...

#cheminformatics #AppleSilicon #QSAR #QSPR #alvaDesc #Alvascience #chemsky

Learn how to import external variables and define your target in alvaModel for QSAR/QSPR modeling.
📽️ youtube.com/watch?v=Nb-8...

📘 More on alvaModel: alvascience.com/alvamodel/

#QSAR #QSPR #Cheminformatics #alvaModel #alvascience #chemsky #machinelearning

🎥 New video: Learn how to select molecular descriptors in alvaDesc – blocks, sub-blocks, filters, and custom sets for your QSAR/QSPR models.

📺 youtu.be/cTc99jg0RM4
🔗 alvascience.com/alvadesc

#alvaDesc #QSAR #Cheminformatics #MolecularDescriptors #Alvascience #QSPR #chemsky

📺 Watch the video: youtube.com/watch?v=UJoJ...
📖 Learn more about alvaModel: alvascience.com/alvamodel/
📂 Explore example projects: alvascience.com/model-studies/

#alvaModel #alvaRunner #QSAR #QSPR #PredictiveModeling #ModelDeployment #Cheminformatics #Alvascience

A new study presents ChemTastesPredictor, an ML model for taste classification using alvaMolecule & alvaDesc for structure curation and descriptor generation.

📖 Read more: doi.org/10.1016/j.ch...
🔗 Learn more: www.alvascience.com

#QSAR #QSPR #MachineLearning

🎥 Missed our QSAR/QSPR Webinar?

Watch the full Alvascience workflow in action - from data curation to model deployment using alvaMolecule, alvaDesc, alvaModel & alvaRunner.

📺 www.youtube.com/watch?v=zHA9...

#QSAR #QSPR #Cheminformatics #Alvascience #Webinar #alvaModel #alvaDesc #alvaMolecule

📦 alvaRunner allows you to:
✔️ Apply models without retraining
✔️ Ensure reproducibility and consistency
✔️ Share models across teams

📺 Watch the video: youtube.com/watch?v=UJoJ...
📖 More on alvaModel: alvascience.com/alvamodel/
📂 Explore example projects: alvascience.com/model-studies/

#QSAR #QSPR

🎥 Watch our QSAR/QSPR Webinar

Explore the full QSAR workflow with Alvascience tools!
📊 Classification & regression models
✅ Multiple algorithms, cross-validation
📍 Applicability domain & model interpretation

📺 youtube.com/watch?v=zHA9...

#QSAR #QSPR #Cheminformatics #Webinar #chemsky

A new study by Rojas et al. (2025) presents ChemTastesPredictor, an ML model for taste classification using alvaMolecule & alvaDesc for structure curation and descriptor generation.

📖 Read more: doi.org/10.1016/j.ch...
🔗 Learn more: www.alvascience.com

#QSAR #QSPR #MachineLearning #Cheminformatics

The Solubility Challenge (2008, Llinàs et al.) solution by my RhNet2 model.

Preprint: doi.org/10.26434/che...

Trained models: github.com/Shorku/Solub...

#MachineLearning #GraphNeuralNetworks #DeepLearning #ComputationalChemistry #Solubility #QSPR #DrugDesign #QuantumChemistry

AlvaDesc has been used in "Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling" doi.org/10.1016/j.en...

Check alvaDesc at: alvascience.com/alvadesc/

#moleculardescriptors #qsar #cheminformatics #pfas #qspr

🎥 QSAR/QSPR Modeling Webinar

Learn the key steps in QSAR/QSPR modeling, from data curation model deployment using alvaDesc, alvaModel, and alvaRunner.

📺 Watch now: www.youtube.com/watch?v=zHA9...

#QSAR #QSPR #Cheminformatics #MachineLearning #alvaDesc #alvaModel #alvaRunner

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective Accurately predicting plant cuticle–air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current wor...

AlvaDesc has been used in "Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective"
doi.org/10.3390/mole...

Check alvaDesc at: alvascience.com/alvadesc/

#qsar #moleculardescriptors #qspr