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Abstract The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated Na_V1.5 and Ca_V1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, Na_V1.5 and Ca_V1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source athttps://github.com/gregory-kyro/CardioGenAIto facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows. Scientific contribution This work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, Na_V1.5 and Ca_V1.2 channel activity, which can function independently as effective components of virtual screening pipelines. 

Many-body interactions are essential for understanding non-linear optics and ultrafast spectroscopy of materials. Recent first principles approaches based on nonequilibrium Green’s function formalisms, such as the time-dependent adiabatic GW (TD-aGW) approach, can predict nonequilibrium dynamics of excited states including electron-hole interactions. However, the high-dimensionality of the electron-hole kernel poses significant computational challenges. Here, we develop a data-driven low-rank approximation for the electron-hole kernel, leveraging localized excitonic effects in the Hilbert space of crystalline systems to achieve significant data compression through singular value decomposition (SVD). We show that the subspace of non-zero singular values remains small even as the k-grid grows, ensuring computational tractability with extremely dense k-grids. This low-rank property enables at least 95% data compression and an order-of-magnitude speedup of TD-aGW calculations. Our approach avoids intensive training processes and eliminates time-accumulated errors, seen in previous approaches, providing a general framework for high-throughput, nonequilibrium simulation of light-driven dynamics in materials. 

Insect research collections often include outreach drawers displaying specimens to enhance public awareness and access to scientific knowledge at various events. Despite their educational value, there is limited understanding of how these drawers are designed, used, or evaluated for quality. As a first step towards understanding these aspects, we surveyed members of the community who use insect drawers for public outreach. Survey results indicate that curators and collection managers consider outreach drawers important and use them widely at events, though they are rarely assessed beyond aesthetics and/or anecdotal audience feedback. The number and thematic scope of these drawers vary significantly among institutions, from as few as 3 to more than 50, and covering topics from collection history to pollinator conservation. However, few institutions display these collections online, limiting access to in-person events. Their maintenance and development are also often constrained by limited funding and staff availability. To guide decisions and efforts to enhance the educational impact and accessibility of outreach drawers, we introduce a quick-assessment tool based on five criteria: information, relevance, aesthetics, potential for engagement and inspiration. The next step is to apply appropriate tools to measure public engagement with these displays. 

Locomotion on dynamic rigid surface (i.e., rigid surface accelerating in an inertial frame) presents complex challenges for controller design, which are essential to address for deploying humanoid robots in dynamic real-world environments such as moving trains, ships, and airplanes. This paper introduces a real-time, provably stabilizing control approach for humanoid walking on periodically swaying rigid surface. The first key contribution is an analytical extension of the classical angular momentum-based linear inverted pendulum model from static to swaying grounds whose motion period may be different than the robot’s gait period. This extension results in a time-varying, nonhomogeneous robot model, which is fundamentally different from the existing pendulum models. We synthesize a discrete footstep control law for the model and derive a new set of sufficient stability conditions that verify the controller’s stabilizing effect. Finally, experiments conducted on a Digit humanoid robot, both in simulations and on hardware, demonstrate the framework’s effectiveness in addressing bipedal locomotion on swaying ground, even under uncertain surface motions and unknown external pushes. 

Abstract The wind shear stress at the ocean surface drives momentum exchange across the air-sea interface regulating atmospheric and oceanic phenomena. Theoretically, the mean wind stress acts in a reference frame moving with the ocean surface; however, the relative motion between the air and ocean surface layers is conventionally neglected in bulk transfer formulae. Recent developments improving air-sea momentum flux quantification advocate for explicitly defining the air-sea relative wind, especially in the regime of low wind forcing, where surface currents may approach a significant fraction of the total wind speed. Yet, in practice, this new approach is typically applied using opportunistic definitions of the near-surface current. Here, we build on this recent work and propose a general framework for the bulk air-sea momentum flux that directly accounts for vertical current shear and surface waves in quantifying the stress at the interface. Our approach partitions the stress at the interface into viscous skin and (wave) form drag components, each applied to their relevant surface advections, which are quantified using the inertial motions within the sub-surface log layer and the modulation of waves by currents predicted by linear theory, respectively. The efficacy of this approach is demonstrated using an extensive oceanic dataset from the Coastal Endurance Array (Ocean Observatories Initiative) offshore of Newport, Oregon (2017–2023) that includes co-located measurements of direct covariance wind stress, directional wave spectra, and current profiles. As expected, our framework does not alter the overall dependence of momentum flux on mean wind forcing, and we found the largest impacts at relatively low wind speeds. Below 3 m s$$^{-1}$$, accounting for sub-surface shear reduced form drag variation by 40–50% as compared to a current-agnostic approach; as compared to a shear-free current, i.e., slab ocean, a 35% reduction in form drag variation was found. At this wind forcing, neglecting the currents led to systematically overestimating the form stress by 20 to 50%—an effect that could not be captured by using the slab ocean approach. This framework builds on the existing understanding of wind-wave-current interaction, yielding a novel formulation that explicitly accounts for the role of current shear and surface waves in air-sea momentum flux. This work holds significant implications for air-sea coupled modeling in general conditions. 

The nickel(0) complex [Ni(^iPrPN)(COD)] (^iPrPN = 2-[(N-diisopropylphosphino)methylamino]pyridine, COD = 1,5-cyclooctadiene) was an efficient precatalyst for the hydrodefluorination of partially fluorinated pyridines employing pinacolborane (HBPin).