scholarly journals Assessing Methods and Obstacles in Chemical Space Exploration

2020 ◽  
Author(s):  
Shawn Reeves ◽  
Benjamin DiFrancesco ◽  
Vijay Shahani ◽  
Stephen S. MacKinnon ◽  
Andreas Windemuth ◽  
...  
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Monte Carlo Sampling ◽  
Separation Of Concerns ◽  
De Novo Drug Design ◽  
Probabilistic Sampling ◽  
Generative Methods ◽  
Python Package

Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into three main categories: generation, discrimination, and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design tasks. In this report we present Deriver, an open-source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real-world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties, and lower the chance of becoming trapped in local minima. Additionally, filtering molecules for drug-likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored can hinder exploration, but can improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. We intend that Deriver, which is being made freely available, will be helpful to others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns.

scholarly journals Assessing Methods and Obstacles in Chemical Space Exploration

2020 ◽  
Author(s):  
Shawn Reeves ◽  
Benjamin DiFrancesco ◽  
Vijay Shahani ◽  
Stephen S. MacKinnon ◽  
Andreas Windemuth ◽  
...  
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Monte Carlo Sampling ◽  
Separation Of Concerns ◽  
De Novo Drug Design ◽  
Probabilistic Sampling ◽  
Generative Methods ◽  
Python Package

Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into three main categories: generation, discrimination, and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design tasks. In this report we present Deriver, an open-source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real-world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties, and lower the chance of becoming trapped in local minima. Additionally, filtering molecules for drug-likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored can hinder exploration, but can improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. We intend that Deriver, which is being made freely available, will be helpful to others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns.

scholarly journals Assessing Methods and Obstacles in Chemical Space Exploration

2020 ◽  
Author(s):  
Shawn Reeves ◽  
Benjamin DiFrancesco ◽  
Vijay Shahani ◽  
Stephen S. MacKinnon ◽  
Andreas Windemuth ◽  
...  
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Monte Carlo Sampling ◽  
Separation Of Concerns ◽  
De Novo Drug Design ◽  
Probabilistic Sampling ◽  
Generative Methods ◽  
Python Package

Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into three main categories: generation, discrimination, and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design tasks. In this report we present Deriver, an open-source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real-world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties, and lower the chance of becoming trapped in local minima. Additionally, filtering molecules for drug-likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored can hinder exploration, but can improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. We intend that Deriver, which is being made freely available, will be helpful to others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns.

scholarly journals Assessing Methods and Obstacles in Chemical Space Exploration

2020 ◽  
Author(s):  
Shawn Reeves ◽  
Benjamin DiFrancesco ◽  
Vijay Shahani ◽  
Stephen S. MacKinnon ◽  
Andreas Windemuth ◽  
...  
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Monte Carlo Sampling ◽  
Separation Of Concerns ◽  
De Novo Drug Design ◽  
Probabilistic Sampling ◽  
Generative Methods ◽  
Python Package

Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into three main categories: generation, discrimination, and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design tasks. In this report we present Deriver, an open-source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real-world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties, and lower the chance of becoming trapped in local minima. Additionally, filtering molecules for drug-likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored can hinder exploration, but can improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. We intend that Deriver, which is being made freely available, will be helpful to others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns.

scholarly journals REINVENT 2.0 – an AI Tool for De Novo Drug Design

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.

scholarly journals REINVENT 2.0 – an AI Tool for De Novo Drug Design

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.

REINVENT 2.0 – an AI Tool for De Novo Drug Design

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.

scholarly journals REINVENT 2.0 – an AI Tool for De Novo Drug Design

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.

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

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.

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 Applications of Autoencoders along with Deep Learning Techniques to generate valid molecules

2021 ◽  
Vol 2070 (1) ◽  
pp. 012125
Author(s):  
T Sesha Sai Aparna ◽  
T Anuradha
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Solution Space ◽  
Threshold Value ◽  
Activation Function ◽  
Generative Models ◽  
Pharmaceutical Chemical ◽  
De Novo Drug Design

Abstract From the moment of identifying the fundamental cause of an illness to its availability in the marketplace, it takes an average of 10 years and almost $2.6 billion dollars to develop a medication. We’re actually hunting for a needle in a haystack, which takes a lot of time, effort, and money. In a solution space of between 1030 and 10100 synthetically viable compounds, we’re seeking for the one molecule that can turn off a disease at the molecular level. The chemical solution space is just too large to adequately screen for the desired molecule. Only a small percentage of the synthetically viable compounds for wet lab research are stored in pharmaceutical chemical repositories. Computational de novo drug design can be used to explore this vast chemical space and develop previously undesigned compounds. Computational drug design can cut the amount of time spent in the discovery phase in half, resulting in a shorter time to market and lower drug prices. Deep learning and artificial intelligence (AI) have opened up new perspectives in cheminformatics, especially in molecules generative models. Recurrent neural networks (RNNs) trained with molecules in the SMILES text format, in particular, are very good at exploring the chemical space. Two baseline models were created for generating molecules, one of the model includes an encoder that takes SMILES as input and then develops a deep generative LSTM model which acts as a hidden layer and the output from layers acts as an input to the decoder. The other baseline model acts the same as the above-mentioned model but it includes latent space, it is simply a representation of compressed data that bring related data points closer together physically. To learn data properties and find simpler data representations for analysis, and weights which are obtained from the previous model to generate more efficient molecules. Then created a custom function to play with the temperature of the softmax activation function which creates a threshold value for the valid molecules to generate. This model enables us to produce new molecules through successful exploration.

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