Provided are a method, an apparatus, and a computer program for predicting interaction between a compound and a protein. A method for predicting interaction between a compound and a protein according to some embodiments of the present disclosure may comprises the steps of: acquiring learning data composed of compound data for learning, protein data for learning, and interaction scores; constructing a deep-learning model by using the acquired learning data; and predicting interaction of a given compound and protein through the constructed deep-learning model. Through the learning of the deep-learning mode with the exclusion of amino acid sequences associated with protein domains having a negative influence on interactions from amino acid sequences of proteins for learning, the interaction between a given compound and protein in the in vivo environment can be accurately predicted.

Provided are a method, an apparatus, and a computer program for predicting interaction between a compound and a protein. A method for predicting interaction between a compound and a protein according to some embodiments of the present disclosure may comprises the steps of: acquiring learning data composed of compound data for learning, protein data for learning, and interaction scores; constructing a deep-learning model by using the acquired learning data; and predicting interaction of a given compound and protein through the constructed deep-learning model. Through the learning of the deep-learning mode with the exclusion of amino acid sequences associated with protein domains having a negative influence on interactions from amino acid sequences of proteins for learning, the interaction between a given compound and protein in the in vivo environment can be accurately predicted.

Systems and methods are provided for building and training machine learning models configured to generate in-domain embeddings and perform multimodal analysis inside the same domain. The models include a first encoder trained to receive input from one or more entities represented in a first modality and to encode the one or more entities in the first modality, such that the first encoder is configured to output a first set of embeddings. The models also include a second encoder trained to receive input from one or more entities represented in the second modality and to encode the one or more entities in the second modality, such that the second encoder is configured to output a second set of embeddings. The models also include a projection layer configured to project the first set of embeddings and the second set of embeddings to a shared contrastive space.

Systems and methods are provided for building and training machine learning models configured to generate in-domain embeddings and perform multimodal analysis inside the same domain. The models include a first encoder trained to receive input from one or more entities represented in a first modality and to encode the one or more entities in the first modality, such that the first encoder is configured to output a first set of embeddings. The models also include a second encoder trained to receive input from one or more entities represented in the second modality and to encode the one or more entities in the second modality, such that the second encoder is configured to output a second set of embeddings. The models also include a projection layer configured to project the first set of embeddings and the second set of embeddings to a shared contrastive space.

A method for making a modified release composition, comprising: selecting a desired active agent and polymer matrix for formulating into a modified release composition; assessing degradation effect on release of the active agent from the composition including plotting polymer molecular weight (M.sub.wr) at onset of active agent release vs. active agent molecular weight (M.sub.wA); predicting performance of multiple potential formulations for the composition based on the degradation assessment and average polymer matrix initial molecular weight (M.sub.wo) to define a library of building blocks; determining the optimal ratio of the building blocks to satisfy a specified release profile; and making a modified release composition based on the optimal ratio determination.

A method for making a modified release composition, comprising: selecting a desired active agent and polymer matrix for formulating into a modified release composition; assessing degradation effect on release of the active agent from the composition including plotting polymer molecular weight (M.sub.wr) at onset of active agent release vs. active agent molecular weight (M.sub.wA); predicting performance of multiple potential formulations for the composition based on the degradation assessment and average polymer matrix initial molecular weight (M.sub.wo) to define a library of building blocks; determining the optimal ratio of the building blocks to satisfy a specified release profile; and making a modified release composition based on the optimal ratio determination.

Systems and methods for computer-aided vaccine design may comprise performing one or more molecular dynamics simulations of a protein assembly having at least one epitope, determining a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations, and predicting the immunogenicity of the protein assembly in response to the fluctuation measurement are disclosed.

Systems and methods for computer-aided vaccine design may comprise performing one or more molecular dynamics simulations of a protein assembly having at least one epitope, determining a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations, and predicting the immunogenicity of the protein assembly in response to the fluctuation measurement are disclosed.

The present invention is directed to a method for generating a library of antigen binding molecules for screening for binding to an epitope of interest, said method comprising: a. selecting a template antigen-binding molecule from a set of possible template antigen binding molecules wherein said selected template does not specifically bind the epitope of interest but is known to specifically bind another epitope; b. selecting at least one residue position in said template antigen-binding molecule for mutation; and c. selecting at least one variant residue to substitute at the at least one residue position selected in b; such that a library containing a plurality of variants of said template is generated.

The present invention is directed to a method for generating a library of antigen binding molecules for screening for binding to an epitope of interest, said method comprising: a. selecting a template antigen-binding molecule from a set of possible template antigen binding molecules wherein said selected template does not specifically bind the epitope of interest but is known to specifically bind another epitope; b. selecting at least one residue position in said template antigen-binding molecule for mutation; and c. selecting at least one variant residue to substitute at the at least one residue position selected in b; such that a library containing a plurality of variants of said template is generated.