S. Banerjee

L. Domingo, M. Chehimi, S. Banerjee et al.

The field of drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins, especially when such proteins directly influence disease progression. However, estimating binding affinity demands significant financial and computational resources. While state-of-the-art methodologies employ classical machine learning (ML) techniques, emerging hybrid quantum machine learning (QML) models have shown promise for enhanced performance, owing to their inherent parallelism and capacity to manage exponential increases in data dimensionality. Despite these advances, existing models encounter issues related to convergence stability and prediction accuracy. This paper introduces a novel hybrid quantum-classical deep learning model tailored for binding affinity prediction in drug discovery. Specifically, the proposed model synergistically integrates 3D and spatial graph convolutional neural networks within an optimized quantum architecture. Simulation results demonstrate a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance compared to previous classical approaches.

S. Banerjee, J. Harrison, P. M. Furlong et al.

Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover. This paper presents a meta-learning-based approach to adapt probabilistic predictions of rover dynamics by augmenting a nominal model affine in parameters with a Bayesian regression algorithm (P-ALPaCA). A regularization scheme is introduced to encourage orthogonality of nominal and learned features, leading to interpretable probabilistic estimates of terrain parameters in varying terrain conditions.

A. Roy, A. P. Misra, S. Banerjee

Cloud computing is recognized as one of the most promising solutions to information technology, e.g., for storing and sharing data in the web service which is sustained by a company or third party instead of storing data in a hard drive or other devices. It is essentially a physical storage system which provides large storage of data and faster computing to users over the Internet. In this cloud system, the third party allows to preserve data of clients or users only for business purpose and also for a limited period of time. The users are used to share data confidentially among themselves and to store data virtually to save the cost of physical devices as well as the time. In this paper, we propose a discrete dynamical system for cloud computing and data management of the storage service between a third party and users. A framework, comprised of different techniques and procedures for distribution of storage and their implementation with users and the third party is given. For illustration purpose, the model is considered for two users and a third party, and its dynamical properties are briefly analyzed and discussed. It is shown that the discrete system exhibits periodic, quasiperiodic and chaotic states. The latter discerns that the cloud computing system with distribution of data and storage between users and the third party may be secured. Some issues of data security are discussed and a random replication scheme is proposed to ensure that the data loss can be highly reduced compared to the existing schemes in the literature.