Advanced Methods And Systems For Determining Properties Of A Molecule With Machine Learning

A computer-based system for determining properties of a molecule in an environment, the system comprising:

• a processor;
• and
• a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the system to:
• construct one or more three-dimensional (3D) structure models indicating positions of atoms of the molecule;
• and
• determine the properties of the molecule in the environment by, for each 3D structure model of the constructed one or more 3D structure models:
• generating a surface model representing the environment, wherein the surface model includes a plurality of segments and the generated surface model defines a relationship between the indicated positions of the atoms of the 3D structure model and the plurality of segments;
• and
• predicting, using a machine learning model, charge and chemical potential of each segment of the plurality of segments based on the 3D structure model and the generated surface model.

A non-transitory computer program product for determining properties of a molecule in an environment, the computer program product executed by a server in communication across a network with one or more clients and comprising:

• a computer-readable medium, the computer readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to:
• construct one or more three-dimensional (3D) structure models indicating positions of atoms of the molecule;
• and
• determine the properties of the molecule in the environment by, for each 3D structure model of the constructed one or more 3D structure models:
• generating a surface model representing the environment, wherein the surface model includes a plurality of segments and the generated surface model defines a relationship between the indicated positions of the atoms of the 3D structure model and the plurality of segments;
• and
• predicting, using a machine learning model, charge and chemical potential of each segment of the plurality of segments based on the 3D structure model and the generated surface model.