An inferring device includes one or more memories and one or more processors. The one or more processors are configured to acquire a plurality of latent variables; generate a plurality of structural formulas by inputting the plurality of latent variables, respectively, in a model; and calculate a plurality of scores by evaluating the plurality of structural formulas, respectively. The one or more processors execute processing of the acquisition of the plurality of latent variables, the generation of the plurality of structural formulas, and the calculation of the plurality of scores, at least two times or more. The one or more processors acquire, based on the acquired plurality of latent variables and the calculated plurality of scores, the plurality of latent variables in any of the execution of at least second time or thereafter.

Characteristics of proteins, peptides, and/or peptoids can be determined via two-dimensional correlation spectroscopy and/or two-dimensional co-distribution spectroscopies. Spectral data of the proteins, peptides, and/or peptoids can be obtained with respect to an applied perturbation. two-dimensional co-distribution analysis can be applied to generate an asynchronous co-distribution plot for the proteins, peptides, and/or peptoids to define the population of proteins in solution. In the two-dimensional asynchronous plot, a cross peak can be identified as correlating with an auto peak in the two-dimensional correlation synchronous plot associated with aggregation of the proteins, peptides, and/or peptoids. The two-dimensional asynchronous cross peak can be used to determine an order of a distributed presence of spectral intensities with respect to the applied perturbation. For example, for two wavenumbers v.sub.1 and v.sub.2, the value of the cross peak corresponding to the two wavenumbers can indicate a presence of spectral intensity at v.sub.1 relative to the presence of spectral intensity at v.sub.2.

Characteristics of proteins, peptides, and/or peptoids can be determined via two-dimensional correlation spectroscopy and/or two-dimensional co-distribution spectroscopies. Spectral data of the proteins, peptides, and/or peptoids can be obtained with respect to an applied perturbation. two-dimensional co-distribution analysis can be applied to generate an asynchronous co-distribution plot for the proteins, peptides, and/or peptoids to define the population of proteins in solution. In the two-dimensional asynchronous plot, a cross peak can be identified as correlating with an auto peak in the two-dimensional correlation synchronous plot associated with aggregation of the proteins, peptides, and/or peptoids. The two-dimensional asynchronous cross peak can be used to determine an order of a distributed presence of spectral intensities with respect to the applied perturbation. For example, for two wavenumbers v.sub.1 and v.sub.2, the value of the cross peak corresponding to the two wavenumbers can indicate a presence of spectral intensity at v.sub.1 relative to the presence of spectral intensity at v.sub.2.

The Artificial Intelligence engine can perform one or more operations. A query can be submitted to the Artificial Intelligence engine to search directly for a set of targeted properties for an unnamed molecule having the set of targeted properties. An indication of a structure of one or more candidate molecules found to have the set of targeted properties with the Artificial Intelligence engine is generated by applying one or more machine learning algorithms. The indication of the structure of the one or more candidate molecules found to satisfy the set of targeted properties in 3-dimensional space is supplied to a user in response to the query for the set of targeted properties to the Artificial Intelligence engine.

The Artificial Intelligence engine can perform one or more operations. A query can be submitted to the Artificial Intelligence engine to search directly for a set of targeted properties for an unnamed molecule having the set of targeted properties. An indication of a structure of one or more candidate molecules found to have the set of targeted properties with the Artificial Intelligence engine is generated by applying one or more machine learning algorithms. The indication of the structure of the one or more candidate molecules found to satisfy the set of targeted properties in 3-dimensional space is supplied to a user in response to the query for the set of targeted properties to the Artificial Intelligence engine.

The technology disclosed relates to training a pathogenicity predictor. In particular, the technology disclosed relates to accessing a gapped training set that includes respective gapped protein samples for respective positions in a proteome, accessing a non-gapped training set that includes non-gapped benign protein samples and non-gapped pathogenic protein samples, generating respective gapped spatial representations for the gapped protein samples, and generating respective non-gapped spatial representations for the non-gapped benign protein samples and the non-gapped pathogenic protein samples, training a pathogenicity predictor over one or more training cycles and generating a trained pathogenicity predictor, wherein each of the training cycles uses as training examples gapped spatial representations from the respective gapped spatial representations and non-gapped spatial representations from the respective non-gapped spatial representations, and using the trained pathogenicity classifier to determine pathogenicity of variants.

The present disclosure relates to a method of protein structure and amino acid residue interaction prediction based on saturation suppressor mutagenesis screening of a protein of interest. The method of the instant disclosure can be adapted for multi-protein complexes, and is useful where crystal structure of a protein of interest is not available.

The present disclosure relates to a method of protein structure and amino acid residue interaction prediction based on saturation suppressor mutagenesis screening of a protein of interest. The method of the instant disclosure can be adapted for multi-protein complexes, and is useful where crystal structure of a protein of interest is not available.

Provided are variants of a chimeric anti-EGFR antibody. In various embodiments, the variants exhibit substantially improved thermostabilities and/or substantially higher levels of humanness, while retaining binding affinity near the parental level. The consistently high quality of the turnkey CoDAH designs, over a whole panel of variants, suggests that a computationally-directed approach encapsulates key determinants of antibody structure and function.

Methods for selecting a cancer patient for immunotherapy comprise establishing a total passenger gene mutation burden from a tumor of a cancer patient, generating a background distribution for the mutational burden of the tumor, normalizing the total passenger gene mutation burden against the background distribution, and categorizing the cancer patient as an immunotherapy responder when the total passenger gene mutation burden is greater than the mean of the background distribution. When the cancer patient is an immunotherapy responder, the patient may be administered an immunotherapy regimen that comprises activation/inhibition of T cell receptors that promote T cell activation and/or prolong immune cytolytic activities.