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.
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News
Added the model for prediction of CO2 solubility in deep eutectic solvents, melting points, viscosity and density of deep eutectic solvents
Added the model for prediction of drug solubility in supercritical CO2
Added the model for retrosynthesis of oligopyrroles
Added the model for prediction of MIC of ionic liquids (S. aureus, E. coli and P. aeruginosa)
Added the model for prediction of absorption maximum and IC50 of Pt-BODIPY
Added the model for prediction of absorption maximum of meso-carbazole substituted porphyrin complexes
Added the models for prediction of electrical conductivity, viscosity, density, surface tension and sound velocity of ionic liquids
Added the model for prediction of absorption properties of various dyes
Added the models for prediction of melting point, decomposition and glass-transition temperatures of ionic liquids and mixtures
Added the model for prediction of yield of pyrroles and dipyrromethanes condensation reactions with aldehydes based on RFR/ECFP
Added the model for 11B NMR chemical shift prediction based on RFR/ISIDA Fragments
Publications
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