Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery

Bhatia, Amandeep Singh; Saggi, Mandeep Kaur; Kais, Sabre

doi:10.1021/acs.jcim.3c01079

Cited by 10 publications

(5 citation statements)

References 33 publications

(43 reference statements)

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“…19−28 More specifically, there have been several QML works that are trained to predict toxicity. 10,29,30 Despite the success of these QML models, the limitations that plague noisy intermediate-scale quantum (NISQ) devices, such as decoherence and gate errors, are still a sobering reality.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Leveraging the principles of superposition and entanglement, quantum computing offers a new framework that potentially expands computational boundaries for ML. Recent work suggests that quantum ML (QML) models are more learnable and generalize better to unseen data than classical networks. − In pursuit of these potential advantages, researchers have built numerous QML models to address a range of chemical and biological problems. − More specifically, there have been several QML works that are trained to predict toxicity. ,, Despite the success of these QML models, the limitations that plague noisy intermediate-scale quantum (NISQ) devices, such as decoherence and gate errors, are still a sobering reality.…”

Section: Introductionmentioning

confidence: 99%

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Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction

Smaldone,

Batista

2024

J. Chem. Theory Comput.

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Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying scriptO ( n 3 ) matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity scriptO ( n 2 ) . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model’s fully classical scriptO ( n 3 ) analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction

Smaldone,

Batista

2024

J. Chem. Theory Comput.

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“…Since the introduction of quantum neural network [16,17], many researchers focused on the quantum machine learning algorithms that have an instant influence on real-time applications, e.g. quantum chemistry [18][19][20] or optimization [21] to leverage the power of quantum computers.…”

Section: Introductionmentioning

confidence: 99%

Federated quanvolutional neural network: a new paradigm for collaborative quantum learning

Bhatia,

Kais,

Alam

2023

Quantum Sci. Technol.

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In recent years, the concept of federated machine learning has been actively driven by scientists to ease the privacy concerns of data owners. Currently, the combination of machine learning and quantum computing technologies is a hot industry topic and is positioned to be a major disruptor. It has become an effective new tool for reshaping several industries ranging from healthcare to finance. Data sharing poses a significant hurdle for large-scale machine learning in numerous industries. It is a natural goal to study the advanced quantum computing ecosystem, which will be comprised of heterogeneous federated resources. In this work, the problem of data governance and privacy is handled by developing a quantum federated learning approach, that can be efficiently executed on quantum hardware in the noisy intermediate-scale quantum (NISQ) era. We present the federated hybrid quantumclassical algorithm called a quanvolutional neural network with distributed training on different sites without exchanging data. The hybrid algorithm requires small quantum circuits to produce meaningful features for image classification tasks, which makes it ideal for near-term quantum computing. The primary goal of this work is to evaluate the potential benefits of hybrid quantum-classical and classical-quantum convolutionalneural networks on non-independently and non-identically partitioned (Non-IID) and real-world data partitioned datasets among several healthcare institutions/clients. We investigated the performance of a collaborative quanvolutional neural network on two medical machine learning datasets, COVID-19 and MedNIST. Extensive experiments are carried out to validate the robustness and feasibility of the proposed quantum federated learning framework. Our findings demonstrate a decrease of 2-39% times in necessary communication rounds compared to federated stochastic gradient descent approach. The hybrid federated framework maintained a high classification testing accuracy and generalizability, even in scenarios where the medical data is unevenly distributed among clients.

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“…Recently, as quantum computing technology has progressed significantly, accelerated pharmaceutical development is poised to gain additional advantages from this advancement in quantum computing technology . The recent progress in AI/ML models for quantum computing has opened up numerous possibilities to broaden the potential applications of machine learning in areas such as drug discovery, toxicology, and the design of dosage forms. − The use of these innovative technologies and advanced machine learning algorithms has sparked significant enthusiasm and expectation regarding the potential of AI to transform the pharmaceutical industry.…”

mentioning

confidence: 99%

“…For instance, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks, and variational autoencoders can be applied widely in pharmaceutical development. Particularly the deep neural network model can solve some of the current challenges, thereby helping to improve the performance of pharmacokineticpharmacodynamic (PKPD), physiologically based pharmacokinetic (PBPK) modeling, and mechanistic quantitative systems pharmacology (QSP) models to support early drug discovery and development, , as well as the prediction of drug compound in the process of absorption, distribution, metabolism, and excretion (ADME) …”

mentioning

confidence: 99%