Toward efficient generation, correction, and properties control of unique drug-like structures.

Efficient design and screening of the novel molecules is a major challenge in drug and material design. This paper focuses on a multi-stage pipeline, in which several deep neural network models are combined to map discrete molecular representations into continuous vector space to later generate from it new molecular structures with desired properties. Here, the Attention-based Sequence-to-Sequence model is added to "spellcheck" and correct generated structures, while the oversampling in the continuous space allows generating candidate structures with desired distribution for properties and molecular descriptors, even for a small reference datasets. We further use computer simulation to validate the desired properties in the numerical experiment. With the focus on the drug design, such a pipeline allows generating novel structures with a control of Synthetic Accessibility Score and a series of metrics that assess the drug-likeliness. Our code is available at https://github.com/SoftServeInc/novel-molecule-generation.

Dynamics of a Many-Body-Localized System Coupled to a Bath.

Coupling a many-body-localized system to a dissipative bath necessarily leads to delocalization. Here, we investigate the nature of the ensuing relaxation dynamics and the information it holds on the many-body-localized state. We formulate the relevant Lindblad equation in terms of the local integrals of motion of the underlying localized Hamiltonian. This allows us to map the quantum evolution deep in the localized state to tractable classical rate equations. We consider two different types of dissipation relevant to systems of ultracold atoms: dephasing due to inelastic scattering on the lattice lasers and particle loss. Our approach allows us to characterize their different effects in the limiting cases of weak and strong interactions.

Robust deep learning pipeline for PVC beats localization.

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

Reversible first-order transition in Pauli percolation.

Percolation plays an important role in fields and phenomena as diverse as the study of social networks, the dynamics of epidemics, the robustness of electricity grids, conduction in disordered media, and geometric properties in statistical physics. We analyze a new percolation problem in which the first-order nature of an equilibrium percolation transition can be established analytically and verified numerically. The rules for this site percolation model are physical and very simple, requiring only the introduction of a weight W(n)=n+1 for a cluster of size n. This establishes that a discontinuous percolation transition can occur with qualitatively more local interactions than in all currently considered examples of explosive percolation; and that, unlike these, it can be reversible. This greatly extends both the applicability of such percolation models in principle and their reach in practice.