Shortlist selection model for active learning

Abstract

Method(s) and apparatus are provided for generating a selection model based on a machine learning (ML) technique, the selection model for selecting a shortlist of compounds requiring validation with aparticular property. An iterative procedure or feedback loop for generating the selection model may include: receiving a prediction result list output from a property model for predicting whether a plurality of compounds are associated with a particular property and an property model score; retraining the selection model based on the property model score and / or the prediction result list; selecting a shortlist of compounds using the retrained selection model from the plurality of compounds associated with the prediction result list; sending the selected shortlist of compounds for validation with the particular property, where another ML technique is used to update the property model based on the validation; repeating the receiving and retraining of the selection model until determining the selection model has been validly trained.

Description

technical field [0001] The present application relates to devices, systems and methods for selecting a candidate list of compounds for active learning and model validation. Background technique [0002] Informatics is the application of computer and information technology and resources in one or more academic and / or scientific fields to interpret data. Chemoinformatics and bioinformatics include the application of computer and information technology and resources to interpret chemical and / or biological data. This may include solving and / or simulating processes and / or problems in the fields of chemistry and / or biology. For example, these computer and information technologies and resources can convert data into information and information into knowledge for the rapid creation of compounds and / or to make improvement decisions. [0003] Machine learning techniques are computational methods that can be used to design complex analytical models and algorithms to solve complex pr...

AI Technical Summary

Problems solved by technology

While there are many ML techniques available or selected for predicting whether a compound has a specific property or characteristic, there is often a lack of training data to properly train the ML technique to generate a properly trained ML model to predict whether a compound has a specific property, such The model is referred to as the property model in this paper

If ML techniques are used to generate property models based on insufficiently labeled training data, the resulting property models may not reliably predict whether or not a particular property is present for most compounds

[0004] Generating labeled training datasets for training ML techniques to generate accurate and reliable property models to predict whether a compound has a specific property is expensive, time-consuming and prone to human error

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