ML-Boosted Giga-Scale Docking

Unlock the potential to speed up Drug Discovery with our cutting-edge machine learning-boosted docking approach. We seamlessly blend advanced computational techniques and active learning, optimizing the docking process to explore chemical space faster and with less computational expenses. Here at Chemspace, we offer efficient exploration of giga-scale chemical spaces utilizing a combination of docking and machine learning. This approach enables the exploration of chemical spaces consisting of billions of compounds while docking only a fraction of them. Multiple publications have proven the efficiency of this method in detecting the top-scoring compounds. [1,2,3] ML-boosted giga scale docking Schema ML-boosted giga scale docking Schema ML-boosted giga scale docking Schema The project starts with the development of a docking model based on your protein of interest. Our professionals evaluate the quality of the protein structure in order to proceed with docking. The first fraction of compounds is selected randomly from the chemical space of choice to sample different regions. These compounds are docked, and a machine-learning model is trained on the results. This model is used to predict the docking scores on the rest of the space. The next compounds for docking are selected based on the predictions of the model – top compounds are picked based on the predicted score. The process is repeated iteratively until most of the potentially active compounds are retained. Usually, this requires docking of approximately 1% of the library or less. As a result, you receive all the docked compounds with the corresponding docking scores.
Available chemical spaces
  • Chemspace In-Stock compounds (~7.1M)
  • Enamine REAL Database (6B molecules)
  • Custom selection from Enamine xREAL Space according to your requirements (specific physicochemical properties, scaffolds, diversity set, etc.)
Project requirements
For us to complete this workflow on your target of interest, a well-defined protein structure must be available (X-ray, Cryo-EM, NMR, homology models). Additional requirements for compound set pre-filtering can be discussed upon request.

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References
  1. Sivula, T.; Yetukuri, L.; Kalliokoski, T.; Käsnänen, H.; Poso, A.; Pöhner, I. Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries. J. Chem. Inf. Model. 2023, 63 (18), 5773–5783. https://doi.org/10.1021/acs.jcim.3c01239.
  2. Yang, Y.; Yao, K.; Repasky, M. P.; Leswing, K.; Abel, R.; Shoichet, B. K.; Jerome, S. V. Efficient Exploration of Chemical Space with Docking and Deep Learning. J. Chem. Theory Comput. 2021, 17 (11), 7106–7119. https://doi.org/10.1021/acs.jctc.1c00810.
  3. Graff, D. E.; Shakhnovich, E. I.; Coley, C. W. Accelerating High-Throughput Virtual Screening through Molecular Pool-Based Active Learning. Chem. Sci. 2021, 12 (22), 7866–7881. https://doi.org/10.1039/d0sc06805e.