Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, José; Grisoni, Francesca; Weskamp, Nils; Schneider, Gisbert

doi:10.1080/17460441.2021.1909567

Cited by 248 publications

(146 citation statements)

References 185 publications

Supporting

Mentioning

144

Contrasting

Unclassified

Order By: Relevance

“…Nonetheless, some of the de novo generated molecules appear chemically feasible and attractive, contain innovative molecular scaffolds and deserve further consideration, illustrating the potential of generative models for rapid delivery of testable chemical designs and concepts. 149 …”

Section: Ligand-based Antiviral Drug Discovery Approachesmentioning

confidence: 99%

A critical overview of computational approaches employed for COVID-19 drug discovery

Amaro²,

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Ligand-based Antiviral Drug Discovery Approachesmentioning

confidence: 99%

A critical overview of computational approaches employed for COVID-19 drug discovery

Amaro²,

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…To take a graph-based approach, here we use the graph-based DGMs implemented in GraphINVENT (Mercado et al, 2021a), which use graph neural networks (GNNs) to generate molecular graphs, and combine them with an RL framework as in REINVENT. Graph-based models are not only less explored for deep molecular generation, but also allow direct learning from the graph structure, better handling of complex molecular ring systems, and simpler integration of 3D information (Jiménez-Luna et al, 2021).…”

Section: Molecular Dgmsmentioning

confidence: 99%

De novo drug design using reinforcement learning with graph-based deep generative models

Atance

Diez

Engkvist

et al. 2021

Preprint

View full text Add to dashboard Cite

Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models. Here, we propose a graph-based deep generative model for de novo molecular design using reinforcement learning. We demonstrate how the reinforcement learning framework can successfully fine-tune the generative model towards molecules with various desired sets of properties, even when few molecules have the goal attributes initially. We explored the following tasks: decreasing/increasing the size of generated molecules, increasing their drug-likeness, and increasing protein-binding activity. Using our model, we are able to generate 95% predicted active compounds for a common benchmarking task, outperforming previously reported methods on this metric.

show abstract

“…As part of the revolution in deep learning 43,44 , de novo generative methods have come to the fore (e.g. 42,[45][46][47][48][49][50][51][52][53][54][55] ). These admit the in silico creation of vectors in a high-dimensional 'latent' space ('encoding') and their translation from and into meaningful molecular entities ('decoding').…”

Section: Introductionmentioning

confidence: 99%

MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra

Shrivastava

Swainston

Samanta

et al. 2021

Preprint

View full text Add to dashboard Cite

The ′inverse problem′ of mass spectrometric molecular identification (′given a mass spectrum, calculate the molecule whence it came′) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (′calculate a small molecule′s likely fragmentation and hence at least some of its mass spectrum from its structure alone′) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ′translation′ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ′true′ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ′similar′ to the top hit. In addition to using the ′top hits′ directly, we can produce a rank order of these by ′round-tripping′ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. The ability to create and to ′learn′ millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

show abstract

Artificial intelligence in drug discovery: recent advances and future perspectives

Cited by 248 publications

References 185 publications

A critical overview of computational approaches employed for COVID-19 drug discovery

A critical overview of computational approaches employed for COVID-19 drug discovery

De novo drug design using reinforcement learning with graph-based deep generative models

MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra

Contact Info

Product

Resources

About