Sampling a Subset of Chemical Space with GNN-Based Generative Models

dc.contributor.authorRASTEMO, TOBIAS
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerSchliep, Alexander
dc.contributor.supervisorTavara, Shirin
dc.date.accessioned2020-09-18T13:35:26Z
dc.date.available2020-09-18T13:35:26Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractIn recent years deep neural network models have been used in the field of drug discovery for de novo molecular design. One, somewhat novel, field of deep learning that has seen some use in drug discovery is graph neural networks (GNN:s). This thesis evaluates 6 GNN models for use in molecular graph generation. The evaluation is based on a benchmark introduced by Arús-Pous et al. [1], which measures how well models sample a subset of chemical space. The models are also compared to existing recurrent neural network models (RNN:s), which use string representation of molecules. The best performing GNN models achieve comparable scores to the RNN models, all though the RNN models score higher. Even though the GNN models score slightly lower on two of the training sets, they still show great potential for future use and merit further research. In addition to this, a data loading scheme for PyTorch is introduced, which increases training speed by loading training data from disk efficiently.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301735
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectmachine learningsv
dc.subjectdeep learningsv
dc.subjectgraph neural networkssv
dc.subjectmessage passing neural networksv
dc.subjectde novo molecular designsv
dc.subjectgraph generationsv
dc.titleSampling a Subset of Chemical Space with GNN-Based Generative Modelssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
CSE 20-90 Rastemo.pdf
Storlek:
2.67 MB
Format:
Adobe Portable Document Format
Beskrivning:
Sampling a Subset of Chemical Space with GNN-Based Generative Models
License bundle
Visar 1 - 1 av 1
Bild saknas
Namn:
license.txt
Storlek:
1.14 KB
Format:
Item-specific license agreed upon to submission
Beskrivning: