Invited: Drug Discovery Approaches using Quantum Machine Learning

2021 ◽  
Author(s):  
Junde Li ◽  
Mahabubul Alam ◽  
Congzhou M Sha ◽  
Jian Wang ◽  
Nikolay V. Dokholyan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Quantum Machine Learning

scholarly journals Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

2021 ◽  
Author(s):  
Marcus Wieder ◽  
Josh Fass ◽  
John D. Chodera
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Drug Discovery ◽  
Energy Data ◽  
Quantum Machine Learning ◽  
Computer Aided ◽  
Approved Drugs

The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution....

Quantum Machine Learning for Drug Discovery

2020 ◽  
Author(s):  
Kushal Batra ◽  
Kimberley M. Zorn ◽  
Daniel H. Foil ◽  
Eni Minerali ◽  
Victor O. Gawriljuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Molecular Descriptors ◽  
Quantum Computer ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Scoring Functions ◽  
Biological Targets ◽  
Hybrid Approaches ◽  
Quantum Machine Learning

<p>The growing public and private datasets focused on small molecules screened against biological targets or whole organisms <sup>1</sup> provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design <sup>2-4</sup>, predict on- or off-target effects and create scoring functions <sup>5,6</sup>. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and <i>M. tuberculosis</i>) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.</p>

Quantum Machine Learning Algorithms for Drug Discovery Applications

2021 ◽  
Author(s):  
Kushal Batra ◽  
Kimberley M. Zorn ◽  
Daniel H. Foil ◽  
Eni Minerali ◽  
Victor O. Gawriljuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quantum Machine Learning

Quantum Machine Learning for Drug Discovery

2020 ◽  
Author(s):  
Kushal Batra ◽  
Kimberley M. Zorn ◽  
Daniel H. Foil ◽  
Eni Minerali ◽  
Victor O. Gawriljuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Molecular Descriptors ◽  
Quantum Computer ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Scoring Functions ◽  
Biological Targets ◽  
Hybrid Approaches ◽  
Quantum Machine Learning

<p>The growing public and private datasets focused on small molecules screened against biological targets or whole organisms <sup>1</sup> provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design <sup>2-4</sup>, predict on- or off-target effects and create scoring functions <sup>5,6</sup>. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and <i>M. tuberculosis</i>) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.</p>

scholarly journals Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

2020 ◽  
Author(s):  
Marcus Wieder ◽  
Josh Fass ◽  
John D. Chodera
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Drug Discovery ◽  
Harmonic Oscillator ◽  
Quantum Chemical ◽  
Free Energy Calculations ◽  
Rigid Rotor ◽  
Free Energies ◽  
Quantum Machine Learning ◽  
Computer Aided

AbstractThe computation of tautomer rations of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals Robust in practice: Adversarial attacks on quantum machine learning

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Haoran Liao ◽  
Ian Convy ◽  
William J. Huggins ◽  
K. Birgitta Whaley
Keyword(s):  
Machine Learning ◽  
Quantum Machine Learning

The Drug Factory: Industrializing How New Drugs Are Found

2021 ◽  
pp. 247255522110281
Author(s):  
August Allen ◽  
Lina Nilsson
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Feedback Loops ◽  
New Drugs

In this perspective, the authors paint a vision for industrializing drug discovery with an “atoms and bits” approach. This approach leverages advancements in machine learning, automation, and biological tools to create a platform for drug discovery that de-specializes the output of insights and generates feedback loops to iterate on each success and failure. Recursion Pharmaceuticals, where the authors work, is provided as an example of how one company is attempting to achieve this vision.

scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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