A computer-implemented method for predicting a conformation of a ligand docked into a protein is disclosed. According to some embodiments, the method may include determining one or more poses of the ligand in the protein, the poses being representative conformations of the ligand. The method may also include determining, using a neural network, energy scores of the poses. The method may further include determining a proper conformation for the docked ligand based on the energy scores.

A computer-implemented method for predicting a conformation of a ligand docked into a protein is disclosed. According to some embodiments, the method may include determining one or more poses of the ligand in the protein, the poses being representative conformations of the ligand. The method may also include determining, using a neural network, energy scores of the poses. The method may further include determining a proper conformation for the docked ligand based on the energy scores.

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

Disclosed herein are systems and methods for efficient 3D structure estimation from images of a transmissive object, including cryo-EM images. The method generally comprises, receiving a set of 2D images of a target specimen from an electron microscope, carrying out a reconstruction technique to determine a likely molecular structure, and outputting the estimated 3D structure of the specimen. The described reconstruction technique comprises: establishing a probabilistic model of the target structure; optimizing using stochastic optimization to determine which structure is most likely; and, optionally utilizing importance sampling to minimize computational burden.

Disclosed herein are systems and methods for efficient 3D structure estimation from images of a transmissive object, including cryo-EM images. The method generally comprises, receiving a set of 2D images of a target specimen from an electron microscope, carrying out a reconstruction technique to determine a likely molecular structure, and outputting the estimated 3D structure of the specimen. The described reconstruction technique comprises: establishing a probabilistic model of the target structure; optimizing using stochastic optimization to determine which structure is most likely; and, optionally utilizing importance sampling to minimize computational burden.

The invention relates to an in silico screening method to identify candidate excipients for reducing aggregation of a protein in a formulation. The method combines computational molecular modeling and molecular dynamics simulations to identify sites on a protein where non-specific self-interaction and interaction of different test excipients may occur, determine the relative binding energies of such interactions, and select one or more test excipients that meet specified interaction criteria for use as candidate excipients in empirical screening studies.

The invention relates to an in silico screening method to identify candidate excipients for reducing aggregation of a protein in a formulation. The method combines computational molecular modeling and molecular dynamics simulations to identify sites on a protein where non-specific self-interaction and interaction of different test excipients may occur, determine the relative binding energies of such interactions, and select one or more test excipients that meet specified interaction criteria for use as candidate excipients in empirical screening studies.

A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

A method for calculating binding free energy, where the method includes a plurality of steps each including adding a distance restraint potential between a binding calculation target molecule and a target molecule, wherein the method is a method for calculating binding free energy between the binding calculation target molecule and the target molecule using a computer, and wherein anchor points of the binding calculation target molecule in the plurality of the steps are identical anchor points, and anchor points of the target molecule in the plurality of the steps are different anchor points.