scholarly journals DockStream: a docking wrapper to enhance de novo molecular design

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jeff Guo ◽  
Jon Paul Janet ◽  
Matthias R. Bauer ◽  
Eva Nittinger ◽  
Kathryn A. Giblin ◽  
...  
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Scoring Function ◽  
Generative Models ◽  
Public Data ◽  
Analysis Workflow ◽  
Binding Cavity ◽  
Qsar Models ◽  
De Novo Molecular Design ◽  
Version 2.0

AbstractRecently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream.

scholarly journals Molecular Generators and Optimizers Failure Modes

2021 ◽  
Vol 8 (2) ◽  
pp. 53-62
Author(s):  
Mani Manavalan
Keyword(s):  
Failure Modes ◽  
De Novo ◽  
Molecular Design ◽  
Binary Classification ◽  
Scoring Function ◽  
Growth Factor Receptor ◽  
Generative Models ◽  
Janus Kinase ◽  
De Novo Molecular Design ◽  
Epidermal Growth

In recent years, there has been an uptick in interest in generative models for molecules in drug development. In the field of de novo molecular design, these models are used to make molecules with desired properties from scratch. This is occasionally used instead of virtual screening, which is limited by the size of the libraries that can be searched in practice. Rather than screening existing libraries, generative models can be used to build custom libraries from scratch. Using generative models, which may optimize molecules straight towards the desired profile, this time-consuming approach can be sped up. The purpose of this work is to show how current shortcomings in evaluating generative models for molecules can be avoided. We cover both distribution-learning and goal-directed generation with a focus on the latter. Three well-known targets were downloaded from ChEMBL: Janus kinase 2 (JAK2), epidermal growth factor receptor (EGFR), and dopamine receptor D2 (DRD2) (Bento et al. 2014). We preprocessed the data to get binary classification jobs. Before calculating a scoring function, the data is split into two halves, which we shall refer to as split 1/2. The ratio of active to inactive users. Our goal is to train three bioactivity models with equal prediction performance, one to be used as a scoring function for chemical optimization and the other two to be used as performance evaluation models. Our findings suggest that distribution-learning can attain near-perfect scores on many existing criteria even with the most basic and completely useless models. According to benchmark studies, likelihood-based models account for many of the best technologies, and we propose that test set likelihoods be included in future comparisons.

scholarly journals De novo molecular design and generative models

2021 ◽  
Author(s):  
Joshua Meyers ◽  
Benedek Fabian ◽  
Nathan Brown
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Generative Models ◽  
De Novo Molecular Design

scholarly journals Memory-assisted reinforcement learning for diverse molecular de novo design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Ola Engkvist ◽  
Jürgen Bajorath ◽  
Hongming Chen
Keyword(s):  
Reinforcement Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Scoring Function ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Machine Learning Model ◽  
Low Diversity ◽  
De Novo Molecular Design

Abstract In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards desired properties. Here, we propose a new method to address the low diversity issue in RL for molecular design. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with a desired AlogP value. In a second case study, we applied our method to design ligands for the dopamine type 2 receptor and the 5-hydroxytryptamine type 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more compounds predicted to be active having higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL methods.

scholarly journals Attention-based generative models for de novo molecular design

2021 ◽  
Author(s):  
Orion Dollar ◽  
Nisarg Joshi ◽  
David Beck ◽  
Jim Pfaendtner
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Data Modeling ◽  
Generative Models ◽  
Sequential Data ◽  
De Novo Molecular Design ◽  
Generative Algorithms ◽  
The Impact

Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of...

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

scholarly journals Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Ola Engkvist ◽  
Jürgen Bajorath ◽  
Hongming Chen
Keyword(s):  
Reinforcement Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Scoring Function ◽  
De Novo Design ◽  
Memory Unit ◽  
Chemical Structures ◽  
Low Diversity ◽  
De Novo Molecular Design

<div><div><div><p>In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards particular properties. Here, we propose a new method to address the low diversity issue in RL. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit.</p></div></div></div>

scholarly journals Memory-assisted Reinforcement Learning for Diverse Molecular De Novo Design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Ola Engkvist ◽  
Jürgen Bajorath ◽  
Hongming Chen
Keyword(s):  
Reinforcement Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Scoring Function ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Low Diversity ◽  
De Novo Molecular Design ◽  
Dopamine 2 Receptor

Abstract In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards particular properties. Here, we propose a new method to address the low diversity issue in RL. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with an optimized logP. In a second case study, we applied our method to design ligands for the dopamine 2 receptor and the 5-hydroxytryptamine 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more active compounds and with higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL method.

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

scholarly journals Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Ola Engkvist ◽  
Jürgen Bajorath ◽  
Hongming Chen
Keyword(s):  
Reinforcement Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Scoring Function ◽  
De Novo Design ◽  
Memory Unit ◽  
Chemical Structures ◽  
Low Diversity ◽  
De Novo Molecular Design

<div><div><div><p>In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards particular properties. Here, we propose a new method to address the low diversity issue in RL. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit.</p></div></div></div>

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