This disclosure relates to a method and apparatus for training a prediction model. The method includes: obtaining a training sample set; determining a current training sample from the training sample set based on the training sample weights; inputting current target energy characteristics corresponding to the current training sample into a pre-trained prediction model for basic training to obtain a basic prediction model after completing the basic training; updating the training sample weights corresponding to the training samples based on the basic prediction model; and returning to perform the operation of determining the current training sample from the training sample set based on the updated training sample weights until completing model training to obtain a target prediction model.

Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

An initial conformation generation apparatus includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to generate a model representing a cyclic peptide molecule by identifying Cα atoms of each of a plurality of amino acid residues, by arranging the identified Cα atoms on a circumference, and by adding main chains and side chains of the plurality of amino acid residues, and search for a stable conformation of the cyclic peptide molecule by using the generated model as an initial conformation of the cyclic peptide molecule.

An initial conformation generation apparatus includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to generate a model representing a cyclic peptide molecule by identifying Cα atoms of each of a plurality of amino acid residues, by arranging the identified Cα atoms on a circumference, and by adding main chains and side chains of the plurality of amino acid residues, and search for a stable conformation of the cyclic peptide molecule by using the generated model as an initial conformation of the cyclic peptide molecule.

Provided herein are method of analyzing genomic and metabolomic data from fungi to identify relationships between biosynthetic gene clusters and mass spectrometric features of metabolites.

Provided herein are method of analyzing genomic and metabolomic data from fungi to identify relationships between biosynthetic gene clusters and mass spectrometric features of metabolites.

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 system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.