40 research outputs found
Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
Computational drug design based on artificial intelligence is an emerging
research area. At the time of writing this paper, the world suffers from an
outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus
replication is via protease inhibition. We propose an evolutionary
multi-objective algorithm (EMOA) to design potential protease inhibitors for
SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA
maximizes the binding of candidate ligands to the protein using the docking
tool QuickVina 2, while at the same time taking into account further objectives
like drug-likeliness or the fulfillment of filter constraints. The experimental
part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
In recent years, deep learning has demonstrated promising results in de novo
drug design. However, the proposed techniques still lack an efficient
exploration of the large chemical space. Most of these methods explore a small
fragment of the chemical space of known drugs, if the desired molecules were
not found, the process ends. In this work, we introduce a curiosity-driven
method to force the model to navigate many parts of the chemical space,
therefore, achieving higher desirability and diversity as well. At first, we
train a recurrent neural network-based general molecular generator (G), then we
fine-tune G to maximize curiosity and desirability. We define curiosity as the
Tanimoto similarity between two generated molecules, a first molecule generated
by G, and a second one generated by a copy of G (Gcopy). We only backpropagate
the loss through G while keeping Gcopy unchanged. We benchmarked our approach
against two desirable chemical properties related to drug-likeness and showed
that the discovered chemical space can be significantly expanded, thus,
discovering a higher number of desirable molecules with more diversity and
potentially easier to synthesize. All Code and data used in this paper are
available at https://github.com/amine179/Curiosity-RL-for-Drug-Design
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization problems.
Machine learning techniques have recently been applied to solve such problems.
One example in chemistry is the design of tailor-made organic materials and
molecules, which requires efficient methods to explore the chemical space. We
present a genetic algorithm (GA) that is enhanced with a neural network (DNN)
based discriminator model to improve the diversity of generated molecules and
at the same time steer the GA. We show that our algorithm outperforms other
generative models in optimization tasks. We furthermore present a way to
increase interpretability of genetic algorithms, which helped us to derive
design principles.Comment: 9+3 Pages, 7+4 figures, 2 tables. Comments are welcome! (code is
available at: https://github.com/aspuru-guzik-group/GA