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

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 de novo generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass in vitro 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.

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

10.26434/chemrxiv.13498587 ◽

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

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 de novo generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass in vitro 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.

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LEADD: Lamarckian evolutionary algorithm for de novo drug design

Journal of Cheminformatics ◽

10.1186/s13321-022-00582-y ◽

2022 ◽

Vol 14 (1) ◽

Author(s):

Alan Kerstjens ◽

Hans De Winter

Keyword(s):

Drug Design ◽

Objective Function ◽

Evolutionary Algorithm ◽

De Novo ◽

Molecular Design ◽

Chemical Space ◽

Genetic Operators ◽

Computationally Efficient ◽

De Novo Drug Design ◽

Efficient Manner

AbstractGiven an objective function that predicts key properties of a molecule, goal-directed de novo molecular design is a useful tool to identify molecules that maximize or minimize said objective function. Nonetheless, a common drawback of these methods is that they tend to design synthetically unfeasible molecules. In this paper we describe a Lamarckian evolutionary algorithm for de novo drug design (LEADD). LEADD attempts to strike a balance between optimization power, synthetic accessibility of designed molecules and computational efficiency. To increase the likelihood of designing synthetically accessible molecules, LEADD represents molecules as graphs of molecular fragments, and limits the bonds that can be formed between them through knowledge-based pairwise atom type compatibility rules. A reference library of drug-like molecules is used to extract fragments, fragment preferences and compatibility rules. A novel set of genetic operators that enforce these rules in a computationally efficient manner is presented. To sample chemical space more efficiently we also explore a Lamarckian evolutionary mechanism that adapts the reproductive behavior of molecules. LEADD has been compared to both standard virtual screening and a comparable evolutionary algorithm using a standardized benchmark suite and was shown to be able to identify fitter molecules more efficiently. Moreover, the designed molecules are predicted to be easier to synthesize than those designed by other evolutionary algorithms. Graphical Abstract

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Individual & Collective Human Intelligence in Drug Design: Evaluating the Search Strategy

10.26434/chemrxiv.13354328.v1 ◽

2020 ◽

Author(s):

Giovanni Cincilla ◽

Simone Masoni ◽

Jascha Blobel

Keyword(s):

Artificial Intelligence ◽

Drug Discovery ◽

Drug Design ◽

De Novo ◽

Chemical Space ◽

Human Intelligence ◽

Computer Algorithms ◽

De Novo Drug Design ◽

Research Fields

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find desired molecules (e.g. drug candidates). The objectives of this case study are: assess human intelligence in chemical space exploration problems; compare the performance of individual and collective human intelligence; and contrast human and artificial intelligence achievements in de novo drug design. To our knowledge this is the first time that human intelligence is being evaluated for such a task in drug discovery and, of similar importance, compared to the performance of artificial intelligence (e.g. machine learning, genetic algorithms), giving first insights towards their differences and uniqueness.

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Individual & Collective Human Intelligence in Drug Design: Evaluating the Search Strategy

10.26434/chemrxiv.13354328 ◽

2020 ◽

Author(s):

Giovanni Cincilla ◽

Simone Masoni ◽

Jascha Blobel

Keyword(s):

Artificial Intelligence ◽

Drug Discovery ◽

Drug Design ◽

De Novo ◽

Chemical Space ◽

Human Intelligence ◽

Computer Algorithms ◽

De Novo Drug Design ◽

Research Fields

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find desired molecules (e.g. drug candidates). The objectives of this case study are: assess human intelligence in chemical space exploration problems; compare the performance of individual and collective human intelligence; and contrast human and artificial intelligence achievements in de novo drug design. To our knowledge this is the first time that human intelligence is being evaluated for such a task in drug discovery and, of similar importance, compared to the performance of artificial intelligence (e.g. machine learning, genetic algorithms), giving first insights towards their differences and uniqueness.

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DrugEx v3: Scaffold-Constrained Drug Design with Graph Transformer-based Reinforcement Learning

10.26434/chemrxiv-2021-px6kz ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman W. T. van Vlijmen ◽

Adriaan P. IJzerman ◽

Gerard J. P. van Westen

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Design ◽

De Novo ◽

Chemical Space ◽

Molecular Structures ◽

Rational Drug Design ◽

Graph Representation ◽

General Applicability ◽

De Novo Drug Design

Due to the large drug-like chemical space available to search for feasible drug-like molecules, rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified. With the rapid growth of the application of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work, we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives similar to other known methods and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. In this work, the Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules we proposed a novel positional encoding for each atom and bond based on an adjacency matrix to extend the architecture of the Transformer. Each molecule was generated by growing and connecting procedures for the fragments in the given scaffold that were unified into one model. Moreover, we trained this generator under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, our proposed method was applied to design ligands for the adenosine A2A receptor (A2AAR) and compared with SMILES-based methods. The results demonstrated the effectiveness of our method in that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A2AAR with given scaffolds.

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REINVENT 2.0 – an AI Tool for De Novo Drug Design

10.26434/chemrxiv.12058026.v2 ◽

2020 ◽

Cited By ~ 2

Author(s):

Thomas Blaschke ◽

Josep Arús-Pous ◽

Hongming Chen ◽

Christian Margreitter ◽

Christian Tyrchan ◽

...

Keyword(s):

Drug Discovery ◽

Drug Design ◽

De Novo ◽

Chemical Space ◽

De Novo Design ◽

Interaction Point ◽

De Novo Drug Design ◽

Generative Methods ◽

Collaborative Efforts ◽

Scientific Collaborations

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

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REINVENT 2.0 – an AI Tool for De Novo Drug Design

10.26434/chemrxiv.12058026 ◽

2020 ◽

Author(s):

Thomas Blaschke ◽

Josep Arús-Pous ◽

Hongming Chen ◽

Christian Margreitter ◽

Christian Tyrchan ◽

...

Keyword(s):

Drug Discovery ◽

Drug Design ◽

De Novo ◽

Chemical Space ◽

De Novo Design ◽

Interaction Point ◽

De Novo Drug Design ◽

Generative Methods ◽

Collaborative Efforts ◽

Scientific Collaborations

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

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Artificial Intelligence Guided De Novo Molecular Design Targeting COVID-19

10.26434/chemrxiv.12581075 ◽

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 in silico 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 in silico ligand design. Here, we present a de novo 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.

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Artificial Intelligence (AI) in Drugs and Pharmaceuticals

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207325666211207153943 ◽

2021 ◽

Vol 25 ◽

Author(s):

Adarsh Sahu ◽

Jyotika Mishra ◽

Namrata Kushwaha

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Drug Design ◽

De Novo ◽

Pharmaceutical Companies ◽

Design Activity ◽

De Novo Drug Design ◽

Preclinical Trials ◽

The Cost ◽

Heath Care

: The advancement of computing and technology has invaded all the dimensions of science. Artificial intelligence (AI) is one core branch of Computer Science, which has percolated to all the arenas of science and technology, from core engineering to medicines. Thus, AI has found its way for application in the field of medicinal chemistry and heath care. The conventional methods of drug design have been replaced by computer-aided designs of drugs in recent times. AI is being used extensively to improve the design techniques and required time of the drugs. Additionally, the target proteins can be conveniently identified using AI, which enhances the success rate of the designed drug. The AI technology is used in each step of the drug designing procedure, which decreases the health hazards related to preclinical trials and also reduces the cost substantially. The AI is an effective tool for data mining based on the huge pharmacological data and machine learning process. Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system. The review covers various aspects of AI (Machine learning, Deep learning, Artificial neural networks) in drug design. It also provides a brief overview of the recent progress by the pharmaceutical companies in drug discovery by associating with different AI companies.

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