Molecular Optimization using Deep Learning Extensions of the Transformer for Molecular Optimization
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Forsberg, Marcus
Mattsson, Felix
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Over the recent years, the development in deep learning has provided new approaches
to molecular optimization. Molecular optimization aims to find structurally similar
molecules to a given starting molecule, yielding specified improvements in terms
of different molecular properties. By representing molecules as SMILES, an ap proach to encode molecules as strings of tokens, molecular optimization can be
framed as a machine translation problem, where starting molecules are translated
to molecules with optimized properties. Previous work has shown success for the
Transformer known from natural language processing [1, 2] in the area of molecular
optimization. The thesis covers two extensions of the developed Transformer model
in [1] through curriculum learning and Core-Fixed formulation. Through curriculum
learning, training is structured through a sequence of tasks (curriculum) based on
increasing difficulty. The curriculum could either be determined while training a
model (machine-based) or manually (human heuristic-based). The thesis explores
various approaches to human-based curriculum learning. For the other extension,
Core-Fixed formulation, the thesis provides an approach to reformulating the input
and output of the original model [1], which involves specifying in the input to the
translation model which part that should be fixed (core) and which part that should
be exchanged (R-group) to optimize the complete molecule’s properties. The results
show advantages both in training time and molecule generation performance using
the Core-Fixed formulation. For curriculum learning, the results do not indicate a
clear improvement. The thesis suggests looking into more sophisticated curriculum
learning approaches.
Beskrivning
Ämne/nyckelord
Molecular Optimization , Matched Molecular Pairs , Transformer , AD-MET , Master’s Thesis