REINVENT 2.0 – an AI Tool for De Novo Drug Design

Blaschke, Thomas; Arús‐Pous, Josep; Chen, Hongming; Margreitter, Christian; Tyrchan, Christian; Engkvist, Ola; Papadopoulos, Kostas; Patronov, Atanas

doi:10.26434/chemrxiv.12058026.v3

Cited by 57 publications

(94 citation statements)

References 0 publications

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“…Recurrent neural network-based models of SMILES strings were trained on canonical SMILES or noncanonical SMILES after varying degrees of data augmentation, using either LSTM or GRU architectures. The Python source code used to train the model was derived from our recent benchmarking analysis of generative models of molecules in the low-data regime 27 (https://github.com/skinnider/low-data-generative-models), which was itself adapted from the REINVENT package 24,48 (http://github.com/MarcusOlivecrona/REINVENT). Briefly, each SMILES was converted into a sequence of tokens by splitting the SMILES string into its constituent characters, except for atomic symbols composed of two characters (Br, Cl) and environments within square brackets, such as [nH].…”

Section: Methodsmentioning

confidence: 99%

A Deep Generative Model Enables Automated Structure Elucidation of Novel Psychoactive Substances

Skinnider

Wang²,

Pasin³

et al. 2021

Preprint

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Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police, and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here, we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51%, and a top-10 accuracy of 78%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analyzed by mass spectrometry.

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Section: Methodsmentioning

confidence: 99%

A Deep Generative Model Enables Automated Structure Elucidation of Novel Psychoactive Substances

Skinnider

Wang²,

Pasin³

et al. 2021

Preprint

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“…Generative models that have only been pretrained with prior data (e.g. from STD or AGN) and have not been subjected to RL training (or transfer learning [29]) are referred to as priors or prior agents. STD and AGN are used interchangeably to refer either to the datasets themselves or to the priors resulting from pre-training with the respective dataset.…”

Section: Conventions and Notationmentioning

confidence: 99%

“…To explore the effects of the scoring function composition and the prior, all combinations of the respective parameters shown in Table 1 were considered. Each run with activated diversity filter (DF) [23], [29] was repeated three times to evaluate the stochastic effect of the neural network training.…”

Section: D Similarity Querymentioning

confidence: 99%

“…There were 20 REINVENT training runs in total. For each run the agent was trained for 3000 steps with a learning rate of 0.0001 a batch size of 128 and a product type [29] scoring function, where the score is calculated as a weighted geometric mean of the scoring components 𝑐 , . .…”

Section: D Similarity Querymentioning

confidence: 99%

“…This target has been widely used in de novo generation case studies by us [12], [23]- [25] and other groups [26]- [28]. As the generative model, we used REINVENT 2.0 [29] which is publicly available as open access software [30]. 3D similarity together with 2D similarity scoring are routinely employed in VS and there is evidence of complementarity in the generated hit-lists either when the scoring is applied sequentially or in parallel [31]- [33].…”

Section: Introductionmentioning

confidence: 99%

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De Novo Design with Deep Generative Models Based on 3D Similarity Scoring

Papadopoulos¹,

Giblin²,

Janet³

et al. 2021

Preprint

Self Cite

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<p>We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning. Using Dopamine receptor type 2 (DRD2) as an example and its antagonist haloperidol <b>1</b> as a starting point in a ligand based design context, we have shown in a retrospective study that a 3D similarity enabled generative model can discover new leads in the absence of any other information. It can be efficiently used for scaffold hopping and generation of novel series. 3D similarity based models were compared against 2D QSAR based, indicating a significant degree of orthogonality of the generated outputs and with the former having a more diverse output. In addition, when the two scoring components are combined together for training of the generative model, it results in more efficient exploration of desirable chemical space compared to the individual components. </p>

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Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu,

Obulose

2024

Drug Development Research

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The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well‐known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI‐based models for drug–target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

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REINVENT 2.0 – an AI Tool for De Novo Drug Design

Cited by 57 publications

References 0 publications

A Deep Generative Model Enables Automated Structure Elucidation of Novel Psychoactive Substances

A Deep Generative Model Enables Automated Structure Elucidation of Novel Psychoactive Substances

De Novo Design with Deep Generative Models Based on 3D Similarity Scoring

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

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