Dissertation/ Thesis
Few-shot learning for low data drug discovery
| Title: | Few-shot learning for low data drug discovery |
|---|---|
| Authors: | Vella, Daniel (2022) |
| Publisher Information: | University of Malta Faculty of Information and Communication Technology. Department of Artificial Intelligence |
| Publication Year: | 2022 |
| Collection: | University of Malta: OAR@UM / L-Università ta' Malta |
| Subject Terms: | Drug development -- Computer simulation, Drugs--Structure-activity relationships -- Computer simulation, Ligand binding (Biochemistry) -- Computer simulation, Machine learning |
| Description: | M.Sc.(Melit.) ; Humans exhibit a remarkable ability to learn quickly from just few examples. A child seeing a cat for the first time can effectively identify the animal as a cat upon future encounters. This learning ability is in stark contrast with conventional machine learning (ML) techniques which are data hungry. This data requirement poses a challenge for the application of ML for virtual screening (VS) in drug discovery. The main goal in ligand-based VS (LBVS) is to identify active molecules that exhibit desired therapeutic activity against a biological target, based on information on known ligands against these targets. Data acquisition on a compound’s activity against a biological target is resource-intensive and difficult to obtain. Hence, the aim of this study is to quantify whether few-shot ML can be effectively used for low-data drug discovery. Meta-learning techniques aim to achieve the learning how to learn ability observed in humans. Therefore, we explore few-shot ML for this problem domain using the Tox21, MUV and the GPCR subset of the DUD-E datasets. In few-shot ML, we train a model using data from a number of experimental assays, and then use this model to generalise for new experimental assays using only a small (1-10 per class) support set. We build on the state of the art work, which is based on Matching Networks, by Altae-Tran et al. (2017), and use their work as a foundation to introduce two new architectures, the Prototypical Networks and Relation Networks, to this domain. Additionally, we also evaluate results using PR-AUC, in addition to ROC-AUC as in the original work, providing better interpretability of the performance of the proposed models on highly imbalanced data. Our results are consistent with those of the state of the art on Tox21 and MUV datasets. To the best of our knowledge, the DUD-E dataset has not been previously explored for the few-shot learning domain. Our application of the Prototypical Networks, improved with the iterative-refinement LSTM, achieves overall better ... |
| Document Type: | master thesis |
| Language: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/108340 |
| Availability: | https://www.um.edu.mt/library/oar/handle/123456789/108340 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Accession Number: | edsbas.9A3D19E2 |
| Database: | BASE |
| Description not available. |