Anton Sinitskiy Email & Phone Number

Cambridge, MA, US

Greater Boston

Teaching a course entitled “Protein Principles in Biotech”, one of the core courses in the Master’s level Biotechnology Program at Northeastern. A highly practice-oriented program, designed to prepare graduates for success in biotech.

Greater Boston

Consulting in Chemistry and Machine Learning. I'm the only founder of this LLC company.

Cambridge, MA

• Optimization of chemical compounds and chemical reactions with ML:
• I designed, trained and deployed new ML models of various architectures (including, but not limited to: 3D CNN, LSTM/GRU/multi-head-attention-based, FCNN, SVM, RF), and engineered appropriate ML descriptors.
• Carried out projects on various chemical and physico-chemical property predictions: potency (protein-ligand interactions), log D (lipophilicity), pKa (acidity), regioselectivity, reaction yield, etc.
• Closely collaborated with project teams to iteratively optimize target properties of drug discovery leads and their synthetic schemes in a computationally guided way.Generative Chemistry:
• Developed a new ML model and internalized several models from the literature to efficiently generate high-quality virtual structures.
• Worked with project teams to create tailor-made computation workflows.

• At Stanford, I developed a deep learning framework for accurate computation of electron densities and energies of organic molecules. Computations with this method are more accurate and take much less time than.
• Sinitskiy, A. V. & Pande, V. S. (2019) Physical machine learning outperforms 'human learning' in Quantum Chemistry. arXiv:1908.00971.
• Sinitskiy, A. V. & Pande, V. S. (2018) Deep neural network computes electron densities and energies of a large set of organic molecules faster than Density Functional Theory (DFT). arXiv:1809.02723.
• Sinitskiy, A. V. & Pande, V. S. (2018) Computer simulations predict high structural heterogeneity of functional state of NMDA receptors. Submitted. Biophys. J., 115, 841-852.
• Sinitskiy, A. V. & Pande, V. S. (2017) Theoretical restrictions on longest implicit timescales in Markov state models of biomolecular dynamics. J. Chem. Phys., 148, 044111.
• Sinitskiy, A. V. & Pande, V. S. (2017) Simulated dynamics of glycans on ligand-binding domain of NMDA receptors reveals strong dynamic coupling between glycans and protein core. J. Chem. Theory Comput., 13, 5496-5505.

• At UChicago, I developed new computational tools and methods and applied them to various molecular and biomolecular systems on scales inaccessible to experimental or previously existing computational techniques. My.
• Sinitskiy, A. V. & Voth, G. A. (2017) Quantum mechanics / coarse-grained molecular mechanics (QM/CG-MM). J. Chem. Phys., 148, 014102.
• Madsen, J.*, Sinitskiy, A. V.*, Li, J.* & Voth, G. A. (2017) Highly coarse-grained representation of transmembrane proteins. J. Chem. Theory Comput., 13, 935-944. [* Authors contributed equally]
• Hocky, G. M., Baker, J. L., Bradley, M. J., Sinitskiy, A. V., De La Cruz, E. M. & Voth, G. A. (2016) Cations stiffen actin filaments by adhering a key structural element to adjacent subunits. J. Phys. Chem. B, 120.
• Sinitskiy, A. V. & Voth G. A. (2015) A reductionist perspective on quantum statistical mechanics: Coarse-graining of path integrals. J. Chem. Phys., 143, 094104.
• Davtyan, A., Dama, J. F., Sinitskiy, A. V. & Voth G. A. (2014) The theory of ultra-coarse-graining. 2. Numerical implementation. J. Chem. Theory Comput., 10, 5265-5275.

Doctor Of Philosophy (Phd), Chemistry

Cand. Econ. Sci. (2007), M.S. (2004), Economics, Gpa 4.00

Bachelor Of Science (B.S.), Chemistry, Gpa 4.00