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Alvascience

@alvascience.bsky.social

590 followers 420 following 320 posts

Software Solutions for Cheminformatics and QSAR https://www.alvascience.com/ #QSAR #cheminformatics #machinelearning #moleculardescriptors #compchem

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Alvascience @alvascience.bsky.social · Jul 2

🚀 We’re pleased to introduce alvaQSAR, a new free web platform for predicting chemical properties using QSAR models.

alvaQSAR simplifies the process from structure input to report generation, no installation needed.

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Alvascience @alvascience.bsky.social · 1d

#descriptoroftheday #moleculardescriptors #qsar #cheminformatics #alvatips #chemsky

3D-MoRSE descriptors have been proposed in "The Coding of the Three-Dimensional Structure of Molecules by Molecular..." via @pubs.acs.org

doi.org/10.1021/ci95...

Check #alvaDesc at: alvascience.com/alvadesc/

Alvascience @alvascience.bsky.social · 1d

Pandey & Roy (2025) apply read-across + ML to predict drug-induced cardiotoxicity using FDA DICTrank data 🧠

Descriptors from #alvaDesc enabled robust and interpretable models.

📄 doi.org/10.1016/j.in...
🔗 alvascience.com/alvadesc/

#QSAR #Cheminformatics #DrugSafety #InSilico #chemsky

Alvascience @alvascience.bsky.social · 2d

#descriptoroftheday #moleculardescriptors #qsar #cheminformatics #alvatips #chemsky

RDF descriptors have been proposed in "Deriving the 3D structure of organic molecules from their infrared spectra" doi.org/10.1016/S092...

Check #alvaDesc at: alvascience.com/alvadesc/

Alvascience @alvascience.bsky.social · 4d

Accessible in alvaQSAR, a free web platform offering predictive models for human health and environmental endpoints, each with detailed scientific documentation.

Try it out at: alvascience.com/alva/qsar/

#alvaQSAR #QSAR #Cheminformatics #Ecotoxicology #InSilico #ToxicityPrediction

alvaQSAR

alvascience.com

Alvascience @alvascience.bsky.social · 4d

🔹 Key features:
• Fully validated following OECD principles (R² ≈ 0.65; Q² ≈ 0.63)
• Built-in applicability domain assessment
• Automatic QPRF reports (PDF) for transparent documentation

Alvascience @alvascience.bsky.social · 4d

🔹 Included models:
• M1_KNN_MD – k-Nearest Neighbors based on molecular descriptors
• M2_KNN_ECFP – k-Nearest Neighbors using molecular fingerprints (ECFP)
• Consensus model – combines both for enhanced accuracy and robustness

Alvascience @alvascience.bsky.social · 4d

Developed from a dataset of 546 organic compounds, these models estimate toxicity as –log(LC₅₀ [mol/L]) and apply validated similarity-based methods for reliable and interpretable predictions (Cassotti et al., 2014).

Alvascience @alvascience.bsky.social · 4d

🦐 Predicting aquatic toxicity with the Daphnia magna QSAR models in alvaQSAR.

The Daphnia magna LC₅₀ (48 h) models provide a quantitative structure–activity relationship (QSAR) approach to predict acute aquatic toxicity according to OECD REACH Code 9.1.1.

Try it out at: alvascience.com/alva/qsar/

Alvascience @alvascience.bsky.social · 4d

In J. Comput.-Aided Mol. Des. Nagare et al. (2025) developed robust QSAR models for FGFR-1 inhibitors, combining computational & experimental validation.
Descriptors from #alvaDesc
🔗 alvascience.com/alvadesc/
📄 doi.org/10.1007/s108...

#QSAR #cheminformatics #FGFR1 #drugdiscovery #chemsky

Alvascience @alvascience.bsky.social · 5d

#descriptoroftheday #moleculardescriptors #qsar #cheminformatics #alvatips #chemsky

Aromaticity index has been proposed in "A new aromaticity index and its application to five-membered ring heterocycles" Tetrahedron doi.org/10.1016/S004...

Check #alvaDesc at: alvascience.com/alvadesc/

Alvascience @alvascience.bsky.social · 5d

Key features:
✅ Consensus modeling (ICP) improved predictivity
✅ Key features: electronegativity, lipophilicity, pyrroles, tertiary amines
✅ Descriptors from #alvaDesc www.alvascience.com/alvadesc/

#QSTR #toxicity #insilico #cheminformatics #3Rs

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doi.org

Alvascience @alvascience.bsky.social · 5d

New study in Chemosphere: QSTR models predict rat oral LD50 for 702 pharmaceuticals, offering an in silico alternative to animal testing (OECD guidelines).

🔗 doi.org/10.1016/j.ch...