892 research outputs found
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph. A key challenge is therefore to model diverse translation
outputs. Our primary contributions include a junction tree encoder-decoder for
learning diverse graph translations along with a novel adversarial training
method for aligning distributions of molecules. Diverse output distributions in
our model are explicitly realized by low-dimensional latent vectors that
modulate the translation process. We evaluate our model on multiple molecular
optimization tasks and show that our model outperforms previous
state-of-the-art baselines
Computer-Aided Multi-Objective Optimization in Small Molecule Discovery
Molecular discovery is a multi-objective optimization problem that requires
identifying a molecule or set of molecules that balance multiple, often
competing, properties. Multi-objective molecular design is commonly addressed
by combining properties of interest into a single objective function using
scalarization, which imposes assumptions about relative importance and uncovers
little about the trade-offs between objectives. In contrast to scalarization,
Pareto optimization does not require knowledge of relative importance and
reveals the trade-offs between objectives. However, it introduces additional
considerations in algorithm design. In this review, we describe pool-based and
de novo generative approaches to multi-objective molecular discovery with a
focus on Pareto optimization algorithms. We show how pool-based molecular
discovery is a relatively direct extension of multi-objective Bayesian
optimization and how the plethora of different generative models extend from
single-objective to multi-objective optimization in similar ways using
non-dominated sorting in the reward function (reinforcement learning) or to
select molecules for retraining (distribution learning) or propagation (genetic
algorithms). Finally, we discuss some remaining challenges and opportunities in
the field, emphasizing the opportunity to adopt Bayesian optimization
techniques into multi-objective de novo design
Balancing Exploration and Exploitation: Disentangled -CVAE in De Novo Drug Design
Deep generative models have recently emerged as a promising de novo drug
design method. In this respect, deep generative conditional variational
autoencoder (CVAE) models are a powerful approach for generating novel
molecules with desired drug-like properties. However, molecular graph-based
models with disentanglement and multivariate explicit latent conditioning have
not been fully elucidated. To address this, we proposed a molecular-graph
-CVAE model for de novo drug design. Here, we empirically tuned the
value of disentanglement and assessed its ability to generate molecules with
optimised univariate- or-multivariate properties. In particular, we optimised
the octanol-water partition coefficient (ClogP), molar refractivity (CMR),
quantitative estimate of drug-likeness (QED), and synthetic accessibility score
(SAS). Results suggest that a lower value increases the uniqueness of
generated molecules (exploration). Univariate optimisation results showed our
model generated molecular property averages of ClogP = 41.07% 0.01% and
CMR 66.76% 0.01% by the Ghose filter. Multivariate property optimisation
results showed that our model generated an average of 30.07% 0.01%
molecules for both desired properties. Furthermore, our model improved the QED
and SAS (exploitation) of molecules generated. Together, these results suggest
that the -CVAE could balance exploration and exploitation through
disentanglement and is a promising model for de novo drug design, thus
providing a basis for future studies
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