scholarly journals Accelerating antibiotic discovery through artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Marcelo C. R. Melo ◽  
Jacqueline R. M. A. Maasch ◽  
Cesar de la Fuente-Nunez
Keyword(s):  
Artificial Intelligence ◽  
Antimicrobial Resistance ◽  
De Novo ◽  
Molecular Design ◽  
Open Science ◽  
Preclinical Research ◽  
Antibiotic Development ◽  
Multiple Dimensions ◽  
Invasive Organisms ◽  
Antibiotic Discovery

AbstractBy targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

scholarly journals Combining Generative Artificial Intelligence and On-Chip Synthesis for De Novo Drug Design

2020 ◽  
Author(s):  
Francesca Grisoni ◽  
Berend Huisman ◽  
Alexander Button ◽  
Michael Moret ◽  
Kenneth Atz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Design ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Amide Bond ◽  
Reaction Products ◽  
De Novo Drug Design ◽  
On Chip

<p>Automation of the molecular design-make-test-analyze cycle speeds up the identification of hit and lead compounds for drug discovery. Using deep learning for computational molecular design and a customized microfluidics platform for on-chip compound synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space defined by known LXRα agonists, and to suggest structural analogs of known ligands and novel molecular cores. To further the design of lead-like molecules and ensure compatibility with automated on-chip synthesis, this chemical space was confined to the set of virtual products obtainable from 17 different one-step reactions. Overall, 25 <i>de novo</i> generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass <i>in vitro</i> activity screening of the crude reaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch re-synthesis, purification, and re-testing of 14 of these compounds confirmed that 12 of them were potent LXRα or LXRβ agonists. These results support the utilization of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.<b></b></p>

scholarly journals Artificial Intelligence Guided De Novo Molecular Design Targeting COVID-19

2020 ◽  
Author(s):  
Srilok Srinivasan ◽  
Rohit Batra ◽  
Henry Chan ◽  
Ganesh Kamath ◽  
Mathew J. Cherukara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
In Silico ◽  
High Throughput Screening ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Computational Cost ◽  
Ligand Design ◽  
Therapeutic Agents ◽  
Docking Simulations

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. Computational docking simulations have traditionally been used for <i>in silico</i> ligand design and remain popular method of choice for high-throughput screening of therapeutic agents in the fight against COVID-19. Despite the vast chemical space (millions to billions of biomolecules) that can be potentially explored as therapeutic agents, we remain severely limited in the search of candidate compounds owing to the high computational cost of these ensemble docking simulations employed in traditional <i>in silico</i> ligand design. Here, we present a <i>de novo</i> molecular design strategy that leverages artificial intelligence to discover new therapeutic biomolecules against SARS-CoV-2. A Monte Carlo Tree Search algorithm combined with a multi-task neural network (MTNN) surrogate model for expensive docking simulations and recurrent neural networks (RNN) for rollouts, is used to sample the exhaustive SMILES space of candidate biomolecules. Using Vina scores as target objective to measure binding of therapeutic molecules to either the isolated spike protein (S-protein) of SARS-CoV-2 at its host receptor region or to the S-protein:Angiotensin converting enzyme 2 (ACE2) receptor interface, we generate several (~100's) new biomolecules that outperform FDA (~1000’s) and non-FDA biomolecules (~million) from existing databases. A transfer learning strategy is deployed to retrain the MTNN surrogate as new candidate molecules are identified - this iterative search and retrain strategy is shown to accelerate the discovery of desired candidates. We perform detailed analysis using Lipinski's rules and also analyze the structural similarities between the various top performing candidates. We spilt the molecules using a molecular fragmenting algorithm and identify the common chemical fragments and patterns – such information is important to identify moieties that are responsible for improved performance. Although we focus on therapeutic biomolecules, our AI strategy is broadly applicable for accelerated design and discovery of any chemical molecules with user-desired functionality.

De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

2018 ◽  
Author(s):  
Oscar Mendez-Lucio ◽  
Benoit Baillif ◽  
Djork-Arné Clevert ◽  
David Rouquié ◽  
Joerg Wichard
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Gene Expression Signature ◽  
Cellular Level ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Gene Expression Signatures

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular <i>de novo</i> design and compound optimization. Herein, we report the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

2018 ◽  
Author(s):  
Oscar Mendez-Lucio ◽  
Benoit Baillif ◽  
Djork-Arné Clevert ◽  
David Rouquié ◽  
Joerg Wichard
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Gene Expression Signature ◽  
Cellular Level ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Gene Expression Signatures

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular <i>de novo</i> design and compound optimization. Herein, we report the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

scholarly journals Combining generative artificial intelligence and on-chip synthesis for de novo drug design

2021 ◽  
Vol 7 (24) ◽  
pp. eabg3338
Author(s):  
Francesca Grisoni ◽  
Berend J. H. Huisman ◽  
Alexander L. Button ◽  
Michael Moret ◽  
Kenneth Atz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Design ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Reaction Products ◽  
De Novo Drug Design ◽  
On Chip ◽  
Lxr Agonists

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

Intelligently Applying Artificial Intelligence in Chemoinformatics

2018 ◽  
Vol 18 (20) ◽  
pp. 1804-1826 ◽  
Author(s):  
Sahil Sharma ◽  
Deepak Sharma
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Graph Mining ◽  
De Novo ◽  
Molecular Design ◽  
Molecular Graph ◽  
Quantitative Structure Activity Relationship ◽  
Chemical Information ◽  
Design Synthesis

The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an indispensable tool for drug designers to mine chemical information from large compound databases for developing drugs at a much faster rate as never before. The applications of AI have gone beyond bioactivity predictions and have shown promise in addressing diverse problems in drug discovery like de novo molecular design, synthesis prediction and biological image analysis. In this article, we provide an overview of all the algorithms under the umbrella of AI, enlist the tools/frameworks required for implementing these algorithms as well as present a compendium of web servers, databases and open-source platforms implicated in drug discovery, Quantitative Structure-Activity Relationship (QSAR), data mining, solvation free energy and molecular graph mining.

scholarly journals Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

2020 ◽  
Author(s):  
Jose Jimenez-Luna ◽  
Miha Skalic ◽  
Nils Weskamp ◽  
Gisbert Schneider
Keyword(s):  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Network Models ◽  
Neural Network Models ◽  
Molecular Features ◽  
Channel Inhibition ◽  
Explainable Artificial Intelligence ◽  
Rational Molecular Design ◽  
Graph Neural Networks

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo molecule generation. However, these models are considered 'black-box' and 'hard-to-debug'. This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, cardiac potassium channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand-target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically-relevant endpoints.

scholarly journals Artificial Intelligence Guided De Novo Molecular Design Targeting COVID-19

2020 ◽  
Author(s):  
Srilok Srinivasan ◽  
Rohit Batra ◽  
Henry Chan ◽  
Ganesh Kamath ◽  
Mathew J. Cherukara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
In Silico ◽  
High Throughput Screening ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Computational Cost ◽  
Ligand Design ◽  
Therapeutic Agents ◽  
Docking Simulations

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. Computational docking simulations have traditionally been used for <i>in silico</i> ligand design and remain popular method of choice for high-throughput screening of therapeutic agents in the fight against COVID-19. Despite the vast chemical space (millions to billions of biomolecules) that can be potentially explored as therapeutic agents, we remain severely limited in the search of candidate compounds owing to the high computational cost of these ensemble docking simulations employed in traditional <i>in silico</i> ligand design. Here, we present a <i>de novo</i> molecular design strategy that leverages artificial intelligence to discover new therapeutic biomolecules against SARS-CoV-2. A Monte Carlo Tree Search algorithm combined with a multi-task neural network (MTNN) surrogate model for expensive docking simulations and recurrent neural networks (RNN) for rollouts, is used to sample the exhaustive SMILES space of candidate biomolecules. Using Vina scores as target objective to measure binding of therapeutic molecules to either the isolated spike protein (S-protein) of SARS-CoV-2 at its host receptor region or to the S-protein:Angiotensin converting enzyme 2 (ACE2) receptor interface, we generate several (~100's) new biomolecules that outperform FDA (~1000’s) and non-FDA biomolecules (~million) from existing databases. A transfer learning strategy is deployed to retrain the MTNN surrogate as new candidate molecules are identified - this iterative search and retrain strategy is shown to accelerate the discovery of desired candidates. We perform detailed analysis using Lipinski's rules and also analyze the structural similarities between the various top performing candidates. We spilt the molecules using a molecular fragmenting algorithm and identify the common chemical fragments and patterns – such information is important to identify moieties that are responsible for improved performance. Although we focus on therapeutic biomolecules, our AI strategy is broadly applicable for accelerated design and discovery of any chemical molecules with user-desired functionality.

scholarly journals De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Benoit Baillif ◽  
Djork-Arné Clevert ◽  
David Rouquié ◽  
Joerg Wichard
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Gene Expression Signature ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Alternative Approach ◽  
Gene Expression Signatures

AbstractFinding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

scholarly journals Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

2020 ◽  
Author(s):  
Jose Jimenez-Luna ◽  
Miha Skalic ◽  
Nils Weskamp ◽  
Gisbert Schneider
Keyword(s):  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Network Models ◽  
Neural Network Models ◽  
Molecular Features ◽  
Channel Inhibition ◽  
Explainable Artificial Intelligence ◽  
Rational Molecular Design ◽  
Graph Neural Networks

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo molecule generation. However, these models are considered 'black-box' and 'hard-to-debug'. This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, cardiac potassium channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand-target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically-relevant endpoints.

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