Individual and collective human intelligence in drug design: evaluating the search strategy

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 predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. The objectives of this case study are to give the first insights towards: the assessment of human intelligence in chemical space exploration problems; compare the performance of individual and collective human intelligence in such a problems; and also contrast some human and artificial intelligence achievements in de novo drug design.

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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.

Download Full-text

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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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.

Download Full-text

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.

Download Full-text

REINVENT 2.0 – an AI Tool for De Novo Drug Design

10.26434/chemrxiv.12058026.v1 ◽

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.

Download Full-text

REINVENT 2.0 – an AI Tool for De Novo Drug Design

10.26434/chemrxiv.12058026.v3 ◽

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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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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Comprehensive Survey of Recent Drug Discovery Using Deep Learning

International Journal of Molecular Sciences ◽

10.3390/ijms22189983 ◽

2021 ◽

Vol 22 (18) ◽

pp. 9983

Author(s):

Jintae Kim ◽

Sera Park ◽

Dongbo Min ◽

Wankyu Kim

Keyword(s):

Deep Learning ◽

Drug Discovery ◽

Drug Design ◽

De Novo ◽

Molecular Structures ◽

De Novo Drug Design ◽

Related Data ◽

Benchmark Datasets ◽

Comprehensive Survey ◽

Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

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

10.26434/chemrxiv.13498587.v1 ◽

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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