Welcome to ChemPredictor,


the online platform that harnesses the power of machine learning algorithms to predict various properties of individual molecules and reactions. Whether you are a student, researcher, or industry professional, ChemPredictor is an essential tool for anyone working in the field of chemistry. With our user-friendly interface, you can get the results you need in just a few seconds, saving you time and effort.

Try it now and see the difference for yourself!

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Individual molecules

Ionic liquids and mixtures

News

Sep 20, 2024 Dec 5, 2023

Added the model for prediction of CO2 solubility in deep eutectic solvents, melting points, viscosity and density of deep eutectic solvents

 Nov 20, 2023

Added the model for prediction of drug solubility in supercritical CO2

 Oct 23, 2023

Added the model for retrosynthesis of oligopyrroles

 Oct 23, 2023

Added the model for prediction of MIC of ionic liquids (S. aureus, E. coli and P. aeruginosa)

 Oct 15, 2023

Added the model for prediction of absorption maximum and IC50 of Pt-BODIPY

 Oct 14, 2023

Added the model for prediction of absorption maximum of meso-carbazole substituted porphyrin complexes

 Oct 7, 2023

Added the models for prediction of electrical conductivity, viscosity, density, surface tension and sound velocity of ionic liquids

 Oct 1, 2023

Added the model for prediction of absorption properties of various dyes

 Aug 28, 2023

Added the models for prediction of melting point, decomposition and glass-transition temperatures of ionic liquids and mixtures

Aug 26, 2023

Added the model for prediction of yield of pyrroles and dipyrromethanes condensation reactions with aldehydes based on RFR/ECFP

 Jul 5, 2023

Added the model for 11B NMR chemical shift prediction based on RFR/ISIDA Fragments

Publications

Lukanov, M. M., Ksenofontova, K. V., Kerner, A. A., & Ksenofontov, A. A. (2025). Machine learning-aided algorithm for predicting spectral properties and cytotoxicity of BODIPY-appended platinum complexes. Optical Materials, 116703

Makarov, D. M., & Kolker, A. M. (2025). Viscosity of deep eutectic solvents: Predictive modeling with experimental validation. Fluid Phase Equilibria, 587, 114217

Makarov, D. M., Kalikin, N. N., & Budkov, Y. A. (2024). Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches. Industrial & Engineering Chemistry Research, 63, 3, 1589-1603

Makarov, D. M., Fadeeva, Y. A., Shmukler, L. E., & Tetko, I. V. (2021). Beware of proper validation of models for ionic Liquids!. Journal of Molecular Liquids, 344, 117722

Makarov, D. M., Fadeeva, Y. A., Shmukler, L. E., & Tetko, I. V. (2022). Machine learning models for phase transition and decomposition temperature of ionic liquids. Journal of Molecular Liquids, 366, 120247

Makarov, D. M., Fadeeva, Y. A., & Shmukler, L. E. (2023). Predictive modeling of physicochemical properties and ionicity of ionic liquids for virtual screening of novel electrolytes. Journal of Molecular Liquids, 123323

Makarov, D. M., Fadeeva, Y. A., Safonova, E. A., & Shmukler, L. E. (2022). Predictive modeling of antibacterial activity of ionic liquids by machine learning methods. Computational Biology and Chemistry, 101, 107775

Makarov, D. M., Fadeeva, Y. A., Golubev, V. A., & Kolker, A. M. (2023). Designing deep eutectic solvents for efficient CO2 capture: A data-driven screening approach. Separation and Purification Technology, 325, 124614

(OA) Yarullin, D. N., Zavalishin, M. N., Gamov, G. A., Lukanov, M. M., Ksenofontov, A. A., Bumagina, N. A., & Antina, E. V. (2023). Prediction of Sensor Ability Based on Chemical Formula: Possible Approaches and Pitfalls. Inorganics, 11(4), 158

Ksenofontov, A. A., Isaev, Y. I., Lukanov, M. M., Makarov, D. M., Eventova, V. A., Khodov, I. A., & Berezin, M. B. (2023). Accurate prediction of 11 B NMR chemical shift of BODIPYs via machine learning. Physical Chemistry Chemical Physics, 25(13), 9472-9481

Ksenofontov, A. A., Lukanov, M. M., & Bocharov, P. S. (2022). Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes?. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 279, 121442

Ksenofontov, A. A., Lukanov, M. M., Bocharov, P. S., Berezin, M. B., & Tetko, I. V. (2022). Deep neural network model for highly accurate prediction of BODIPYs absorption. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 267, 120577

Bichan, N. G., Ovchenkova, E. N., Ksenofontov, A. A., Mozgova, V. A., Gruzdev, M. S., Chervonova, U. V., … & Lomova, T. N. (2022). Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods. Dyes and Pigments, 204, 110470

Dmitry M. Makarov, Michail M. Lukanov, Aleksey I. Rusanov, Nugzar Zh. Mamardashvili, Alexander A. Ksenofontov (2023). Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes. Journal of Computational Science, 102173