Exploring Multi-Objective Deep Reinforcement Learning Methods for Drug Design

Jumaily, Aws Al; Mukaidaisi, Muhetaer; Vu, Andrew; Tchagang, Alain B.; Li, Yifeng

doi:10.1109/cibcb55180.2022.9863052

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Subsequently, a network transmitting graph information deciphers the ultimate molecular form. DeepFMPO, by weighing fragment resemblances in its optimization, attains superior efficacy (Al Jumaily et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation

Liu,

Song,

et al. 2024

Front. Artif. Intell.

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Alzheimer's disease (AD) is a gradually advancing neurodegenerative disorder characterized by a concealed onset. Acetylcholinesterase (AChE) is an efficient hydrolase that catalyzes the hydrolysis of acetylcholine (ACh), which regulates the concentration of ACh at synapses and then terminates ACh-mediated neurotransmission. There are inhibitors to inhibit the activity of AChE currently, but its side effects are inevitable. In various application fields where Al have gained prominence, neural network-based models for molecular design have recently emerged and demonstrate encouraging outcomes. However, in the conditional molecular generation task, most of the current generation models need additional optimization algorithms to generate molecules with intended properties which make molecular generation inefficient. Consequently, we introduce a cognitive-conditional molecular design model, termed PED, which leverages the variational auto-encoder. Its primary function is to adeptly produce a molecular library tailored for specific properties. From this library, we can then identify molecules that inhibit AChE activity without adverse effects. These molecules serve as lead compounds, hastening AD treatment and concurrently enhancing the AI's cognitive abilities. In this study, we aim to fine-tune a VAE model pre-trained on the ZINC database using active compounds of AChE collected from Binding DB. Different from other molecular generation models, the PED can simultaneously perform both property prediction and molecule generation, consequently, it can generate molecules with intended properties without additional optimization process. Experiments of evaluation show that proposed model performs better than other methods benchmarked on the same data sets. The results indicated that the model learns a good representation of potential chemical space, it can well generate molecules with intended properties. Extensive experiments on benchmark datasets confirmed PED's efficiency and efficacy. Furthermore, we also verified the binding ability of molecules to AChE through molecular docking. The results showed that our molecular generation system for AD shows excellent cognitive capacities, the molecules within the molecular library could bind well to AChE and inhibit its activity, thus preventing the hydrolysis of ACh.

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Section: Related Workmentioning

confidence: 99%

PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation

Liu,

Song,

et al. 2024

Front. Artif. Intell.

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“…Existing drug discovery models, such as LigandScout, lack fulfillment of multiobjective properties like drug-likeness and synthetic accessibility scores. Several molecular generative models have been proposed for multiobjective optimizations. − The multiconstraint molecular generation (MCMG) approach is highly effective to generate novel compounds that satisfy multiple property constraints. We used the model-distillation protocol in MCMG to yield a large chemical space, and the scaffold diversity is not enough when multiple constraints are added.…”

Section: Introductionmentioning

confidence: 99%

Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors

Bo,

Duan,

Zou

et al. 2024

J. Chem. Inf. Model.

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Deep generative models have become crucial tools in de novo drug design. In current models for multiobjective optimization in molecular generation, the scaffold diversity is limited when multiple constraints are introduced. To enhance scaffold diversity, we herein propose a local scaffold diversity-contributed generator (LSDC), which can be utilized to generate diverse lead compounds capable of satisfying multiple constraints. Compared to the state-of-the-art methods, molecules generated by LSDC exhibit greater diversity when applied to the generation of inhibitors targeting the NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3). We present 12 molecules, some of which feature previously unreported scaffolds, and demonstrate their reasonable docking binding modes. Consequently, the modification of selected scaffolds and subsequent bioactivity evaluation lead to the discovery of two potent NLRP3 inhibitors, A22 and A14, with IC50 values of 38.1 nM and 44.43 nM, respectively. And the oral bioavailability of compound A14 is very high (F is 83.09% in mice). This work contributes to the discovery of novel NLRP3 inhibitors and provides a reference for integrating AI-based generation with wet experiments.

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Exploring Multi-Objective Deep Reinforcement Learning Methods for Drug Design

Cited by 2 publications

References 19 publications

PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation

PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation

Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors

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