Conditional β-VAE for De Novo Molecular Generation

05/01/2022
by   Ryan J Richards, et al.
0

Deep learning has significantly advanced and accelerated de novo molecular generation. Generative networks, namely Variational Autoencoders (VAEs) can not only randomly generate new molecules, but also alter molecular structures to optimize specific chemical properties which are pivotal for drug-discovery. While VAEs have been proposed and researched in the past for pharmaceutical applications, they possess deficiencies which limit their ability to both optimize properties and decode syntactically valid molecules. We present a recurrent, conditional β-VAE which disentangles the latent space to enhance post hoc molecule optimization. We create a mutual information driven training protocol and data augmentations to both increase molecular validity and promote longer sequence generation. We demonstrate the efficacy of our framework on the ZINC-250k dataset, achieving SOTA unconstrained optimization results on the penalized LogP (pLogP) and QED scores, while also matching current SOTA results for validity, novelty and uniqueness scores for random generation. We match the current SOTA on QED for top-3 molecules at 0.948, while setting a new SOTA for pLogP optimization at 104.29, 90.12, 69.68 and demonstrating improved results on the constrained optimization task.

READ FULL TEXT
research
05/31/2019

Scaffold-based molecular design using graph generative model

Searching new molecules in areas like drug discovery often starts from t...
research
10/01/2019

Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation

Molecule generation is to design new molecules with specific chemical pr...
research
08/18/2022

Improving Small Molecule Generation using Mutual Information Machine

We address the task of controlled generation of small molecules, which e...
research
10/12/2022

Modular Flows: Differential Molecular Generation

Generating new molecules is fundamental to advancing critical applicatio...
research
08/05/2019

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

We describe ChemBO, a Bayesian Optimization framework for generating and...
research
02/01/2021

Neural representation and generation for RNA secondary structures

Our work is concerned with the generation and targeted design of RNA, a ...
research
02/14/2022

Disentangle VAE for Molecular Generation

Automatic molecule generation plays an important role on drug discovery ...

Please sign up or login with your details

Forgot password? Click here to reset