Graph neural networks for automated de novo drug design

Xiong, Jiacheng; Xiong, Zhitao; Chen, Kaixian; Jiang, Hualiang; Zheng, Mingyue

doi:10.1016/j.drudis.2021.02.011

Cited by 116 publications

(71 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also applications of Graph Neural Networks in de novo drug design, such as Xiong et al’s work that considers three aspects: molecule scoring, molecule generation, optimization, and synthesis planning [ 22 ]. Also, the author in [ 23 ] adopted Transformer to generate molecules.…”

Section: Introductionmentioning

confidence: 99%

Designing optimized drug candidates with Generative Adversarial Network

et al. 2022

View full text Add to dashboard Cite

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder–Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder–Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder–Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model’s ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine $$A_ {2a}$$ A 2 a receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

show abstract

Section: Introductionmentioning

confidence: 99%

Designing optimized drug candidates with Generative Adversarial Network

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Graph-structured data is pervasive in diverse real-world applications, e.g., E-commerce [94], [112], health care [36], [51], traffic forecasting [66], [92], and drug discovery [15], [162]. In recent years, a number of graph mining algorithms have been proposed to gain a deeper understanding of such data.…”

Section: Introductionmentioning

confidence: 99%

Fairness in Graph Mining: A Survey

Dong¹,

Ma²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.

show abstract

“…Up to now, fully ML-driven CAMD has mainly focused on drug design (Elton et al, 2019;Xia et al, 2019;Xiong et al, 2021;Gaudelet et al, 2021). A particular reason might be the availability of large training data sets and the incorporation of multiple drug design targets such as logP and drug-likeness in benchmarking platforms such as MOSES (Polykovskiy et al, 2020) and GuacaMol (Brown et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Graph Machine Learning for Design of High-Octane Fuels

Rittig¹,

Ritzert²,

Schweidtmann³

et al. 2022

Preprint

View full text Add to dashboard Cite

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO 2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data.

show abstract

Graph neural networks for automated de novo drug design

Cited by 116 publications

References 67 publications

Designing optimized drug candidates with Generative Adversarial Network

Designing optimized drug candidates with Generative Adversarial Network

Fairness in Graph Mining: A Survey

Graph Machine Learning for Design of High-Octane Fuels

Contact Info

Product

Resources

About