Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

A method and system for calculating the free energy difference between a target state and a reference state. The method includes determining one or more intermediate states using a coupling parameter, performing molecular simulations to obtain ensembles of micro-states for each of the system states, and calculating the free energy difference by an analysis of the ensembles of micro-states of the system states. The method can be particularly suited for calculating physical or non-physical transformation of molecular systems such as ring-opening, ring-closing, and other transformations involving bond breaking and/or formation. A soft bond potential dependent on a bond stretching component of the coupling parameter and different from the conventional harmonic potential is used in the molecular simulations of the system states for the bond being broken or formed during the transformation.

A method and system for calculating the free energy difference between a target state and a reference state. The method includes determining one or more intermediate states using a coupling parameter, performing molecular simulations to obtain ensembles of micro-states for each of the system states, and calculating the free energy difference by an analysis of the ensembles of micro-states of the system states. The method can be particularly suited for calculating physical or non-physical transformation of molecular systems such as ring-opening, ring-closing, and other transformations involving bond breaking and/or formation. A soft bond potential dependent on a bond stretching component of the coupling parameter and different from the conventional harmonic potential is used in the molecular simulations of the system states for the bond being broken or formed during the transformation.

Metallic alloy development has been traditionally based on experimental or theoretical equilibrium phase diagrams and the like. The synthesis, processing and mechanical testing of small and large real samples are a challenging task requiring huge amount of effort in terms of time, money, resource, tedious testing and processing equipment and man-hour for which conventional Calphad calculations etc. alone do not help much in their local structure and related property prediction. Embodiments of the present disclosure provide simulation systems and methods for structure evolution and property prediction Molecular Dynamics (MD) combined with accelerated Monte Carlo techniques, wherein information on atomic elements and composition specific to alloy material is obtained to generate a MD potential file that is further used to generate a 3D structure file by executing a structure equilibration technique. An optimized evolved 3D structure file is then generated that has atomic positions output and/or thermodynamic output for predicting properties.

Metallic alloy development has been traditionally based on experimental or theoretical equilibrium phase diagrams and the like. The synthesis, processing and mechanical testing of small and large real samples are a challenging task requiring huge amount of effort in terms of time, money, resource, tedious testing and processing equipment and man-hour for which conventional Calphad calculations etc. alone do not help much in their local structure and related property prediction. Embodiments of the present disclosure provide simulation systems and methods for structure evolution and property prediction Molecular Dynamics (MD) combined with accelerated Monte Carlo techniques, wherein information on atomic elements and composition specific to alloy material is obtained to generate a MD potential file that is further used to generate a 3D structure file by executing a structure equilibration technique. An optimized evolved 3D structure file is then generated that has atomic positions output and/or thermodynamic output for predicting properties.

In some embodiments, the disclosure is directed to a system that predicts the carbon footprint of an industrial process. In some embodiments, the system is configured to monitor the amount of energy used in one or more process steps in an industrial process. In some embodiments, the system is configured to determine a carbon intensity for each of the one or more process steps. In some embodiments, the system is configured to generate a report including the carbon intensity. In some embodiments, the system is configured to determine the effect different raw material have on each of the one or more processing steps. In some embodiments, the system is configured to generate an optimum blend of raw materials that reduces the carbon intensity of one or more steps. In some embodiments, the system is configured to generate a blend of source fuels that reduces the industrial facilities overall carbon footprint.

In some embodiments, the disclosure is directed to a system that predicts the carbon footprint of an industrial process. In some embodiments, the system is configured to monitor the amount of energy used in one or more process steps in an industrial process. In some embodiments, the system is configured to determine a carbon intensity for each of the one or more process steps. In some embodiments, the system is configured to generate a report including the carbon intensity. In some embodiments, the system is configured to determine the effect different raw material have on each of the one or more processing steps. In some embodiments, the system is configured to generate an optimum blend of raw materials that reduces the carbon intensity of one or more steps. In some embodiments, the system is configured to generate a blend of source fuels that reduces the industrial facilities overall carbon footprint.

Systems and methods are provided for generating a training dataset for training a molecule embedding module using contrastive learning, wherein the definition of similarity is based on molecular scaffold similarity. For example, systems access a molecular dataset and separate the molecular dataset into positive samples and negative samples. Systems then generate a training dataset comprising the positive samples and negative samples. Systems and methods are also provided for using the trained molecule embedding module to generate molecule embeddings and for building an end-to-end machine learning model configured to perform molecular embedding analysis and molecular property prediction, the model comprising the trained molecule embedding module and a property prediction module.

Systems and methods are provided for generating a training dataset for training a molecule embedding module using contrastive learning, wherein the definition of similarity is based on molecular scaffold similarity. For example, systems access a molecular dataset and separate the molecular dataset into positive samples and negative samples. Systems then generate a training dataset comprising the positive samples and negative samples. Systems and methods are also provided for using the trained molecule embedding module to generate molecule embeddings and for building an end-to-end machine learning model configured to perform molecular embedding analysis and molecular property prediction, the model comprising the trained molecule embedding module and a property prediction module.