US 12,087,404 B2
Generating anti-infective design spaces for selecting drug candidates
Francis Lee, Cambridge, MA (US); Jonathan D. Steckbeck, Cranberry Township, PA (US); and Hannes Holste, Los Angeles, CA (US)
Assigned to Peptilogics, Inc., Pittsburgh, PA (US)
Filed by Peptilogics, Inc., Pittsburgh, PA (US)
Filed on Aug. 22, 2022, as Appl. No. 17/892,701.
Application 17/892,701 is a continuation of application No. 17/404,657, filed on Aug. 17, 2021, granted, now 11,424,008.
Application 17/404,657 is a continuation of application No. 17/319,923, filed on May 13, 2021, granted, now 11,403,316.
Application 17/404,657 is a continuation of application No. 17/319,839, filed on May 13, 2021, granted, now 11,403,316, issued on Aug. 2, 2022.
Claims priority of provisional application 63/117,083, filed on Nov. 23, 2020.
Claims priority of provisional application 63/117,068, filed on Nov. 23, 2020.
Prior Publication US 2022/0399082 A1, Dec. 15, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G16B 40/00 (2019.01); G06F 3/14 (2006.01); G06F 9/451 (2018.01); G06F 16/242 (2019.01); G06F 16/248 (2019.01); G06F 16/29 (2019.01); G06N 20/00 (2019.01); G16B 5/00 (2019.01); G16B 10/00 (2019.01); G16B 30/00 (2019.01); G16B 35/00 (2019.01); G16B 45/00 (2019.01); G16C 20/70 (2019.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/40 (2018.01); G06Q 30/0201 (2023.01); G06Q 50/04 (2012.01); G06Q 50/18 (2012.01); G16H 40/20 (2018.01)
CPC G16B 40/00 (2019.02) [G06F 3/14 (2013.01); G06F 9/451 (2018.02); G06F 16/2428 (2019.01); G06F 16/248 (2019.01); G06F 16/29 (2019.01); G06N 20/00 (2019.01); G16B 5/00 (2019.02); G16B 10/00 (2019.02); G16B 30/00 (2019.02); G16B 35/00 (2019.02); G16B 45/00 (2019.02); G16C 20/70 (2019.02); G16H 10/20 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/40 (2018.01); G06Q 30/0201 (2013.01); G06Q 50/04 (2013.01); G06Q 50/184 (2013.01); G16H 40/20 (2018.01)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
generating a design space for a peptide for an application, wherein the generating comprises:
identifying a plurality of sequences for the peptide; and
updating the plurality of sequences by determining, for each of the plurality of sequences, a respective plurality of activities pertaining to the application, wherein the updating produces an updated plurality of sequences each having an updated respective plurality of activities, and wherein the plurality of activities comprises one or more biomedical activities, biochemical activities, or some combination thereof;
generating, based on the updated plurality of sequences each having the updated respective plurality of activities, a solution space within the design space, wherein the solution space comprises a target subset of the updated plurality of sequences each having the updated respective plurality of activities;
using a first machine learning model to process the solution space, performing, using the first machine learning model, one or more trials to identify a candidate drug compound that represents a sequence having at least one level of activity that exceeds one or more threshold levels;
determining one or more metrics of the first machine learning model that performs the one or more trials, wherein the one or more metrics comprise memory usage, graphic processing unit temperature, power usage, processor usage, central processing unit temperature, or some combination thereof; and
comparing the one or more metrics to one or more second metrics of a second machine learning model that performs the one or more trials.