Patents by Inventor Neel MADHUKAR

Neel MADHUKAR has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11955208
    Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.
    Type: Grant
    Filed: August 24, 2022
    Date of Patent: April 9, 2024
    Assignee: CORNELL UNIVERSITY
    Inventors: Olivier Elemento, Kaitlyn Gayvert, Neel Madhukar
  • Publication number: 20220415451
    Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.
    Type: Application
    Filed: August 24, 2022
    Publication date: December 29, 2022
    Applicant: Cornell University
    Inventors: Olivier Elemento, Kaitlyn Gayvert, Neel Madhukar
  • Publication number: 20220392580
    Abstract: A computational model may be used to predict targets of a candidate, or predict candidates that interact with a target. A plurality of pairs may be established, each including a candidate and a respective one of a plurality of controls, each of the plurality of controls known to bind with a target. For each pair, values of at least two datatypes of the candidate may be compared to values of the at least two datatypes of the respective one of the plurality of controls in the pair to generate a similarity score for each of the at least two datatypes of each pair. Similarity scores may be converted to likelihood values indicating likelihood that the candidate and the controls have a shared target based on the respective one of the at least two datatypes. Tests may be performed to validate predictions regarding interactivity of candidates and targets.
    Type: Application
    Filed: August 19, 2022
    Publication date: December 8, 2022
    Applicant: Cornell University
    Inventors: Olivier Elemento, Neel Madhukar
  • Patent number: 11462303
    Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.
    Type: Grant
    Filed: September 12, 2017
    Date of Patent: October 4, 2022
    Assignee: CORNELL UNIVERSITY
    Inventors: Olivier Elemento, Kaitlyn Gayvert, Neel Madhukar
  • Publication number: 20190295685
    Abstract: Systems and methods for computational analysis of chemical data to predict binding targets of a chemical are provided. A plurality of chemical pairs is established, each including a first chemical for which binding targets are to be predicted and a respective one of the second chemicals. For each chemical pair, values of at least two datatypes of the first chemical can be compared to values of the at least two datatypes of the respective one of the plurality of second chemicals in the chemical pair to generate a similarity score. The similarity scores can be converted to a likelihood value. For each chemical pair, a total likelihood value can be determined based on respective likelihood values for each of the at least two datatypes of the chemical pair. A candidate binding target is predicted to bind to the first chemical, based on the total likelihood value of each chemical pair.
    Type: Application
    Filed: July 6, 2017
    Publication date: September 26, 2019
    Applicant: Cornell University
    Inventors: Olivier ELEMENTO, Neel MADHUKAR
  • Publication number: 20190252036
    Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.
    Type: Application
    Filed: September 12, 2017
    Publication date: August 15, 2019
    Applicant: Cornell University
    Inventors: Olivier Elemento, Kaitlyn Gayvert, Neel Madhukar