Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it

Remarks on Legendrian Self-Linking

The Thurston-Bennequin invariant provides one notion of self-linking for any homologically-trivial Legendrian curve in a contact three-manifold. Here we discuss related analytic notions of self-linking for Legendrian knots in Euclidean space. Our definition is based upon a reformulation of the elementary Gauss linking integral and is motivated by ideas from supersymmetric gauge theory. We recover the Thurston-Bennequin invariant as a special case.Comment: 42 pages, many figures; v2: minor revisions, published versio

Gravity Waves on Hot Extrasolar Planets: I. Propagation and Interaction with the Background

We study the effects of gravity waves, or g-modes, on hot extrasolar planets. These planets are expected to possess stably-stratified atmospheres, which support gravity waves. In this paper, we review the derivation of the equation that governs the linear dynamics of gravity waves and describe its application to a hot extrasolar planet, using HD209458 b as a generic example. We find that gravity waves can exhibit a wide range of behaviors, even for a single atmospheric profile. The waves can significantly accelerate or decelerate the background mean flow, depending on the difference between the wave phase and mean flow speeds. In addition, the waves can provide significant heating (~100 to ~1000 K per planetary rotation), especially to the region of the atmosphere above about 10 scale heights from the excitation region. Furthermore, by propagating horizontally, gravity waves provide a mechanism for transporting momentum and heat from the dayside of a tidally locked planet to its nightside. We discuss work that needs to be undertaken to incorporate these effects in current atmosphere models of extrasolar planets.Comment: Accepted for publication in the Astrophysical Journal; 11 pages, 10 figures

Temporal invariance of social-ecological catchments

Natural resources such as waterbodies, public parks, and wildlife refuges attract people from varying distances on the landscape, creating “social-ecological catchments.” Catchments have provided great utility for understanding physical and social relationships within specific disciplines. Yet, catchments are rarely used across disciplines, such as its application to understand complex spatiotemporal dynamics between mobile human users and patchily distributed natural resources. We collected residence ZIP codes from 19,983 angler parties during 2014–2017 to construct seven angler–waterbody catchments in Nebraska, USA. We predicted that sizes of dense (10% utilization distribution) and dispersed (95% utilization distribution) angler–waterbody catchments would change across seasons and years as a function of diverse resource selection among mobile anglers. Contrary to expectations, we revealed that catchment size was invariant. We discuss how social (conservation actions) and ecological (low water quality, reduction in species diversity) conditions are expected to impact landscape patterns in resource use. We highlight how this simple concept and user-friendly technique can inform timely landscape-level conservation decisions within coupled social-ecological systems that are currently difficult to study and understand

The Mummy Explorer—a self-regulated open-access online teaching tool

Background and objectives: Virtual teaching tools have gained increasing importance in recent years. In particular, the COVID-19 pandemic has reinforced the need for media-based and self-regulated tools. What is missing are tools that allow us to interlink highly interdisciplinary fields such as evolutionary medicine and, at the same time, allow us to adapt content to different lectures. Methodology: We designed an interactive online teaching tool, namely, the Mummy Explorer, using open-access software (Google Web Designer), and we provided a freely downloadable template. We tested the tool on students and lecturers of evolutionary medicine using questionnaires and improved the tool according to their feedback. Results: The tool has a modular design and provides an overview of a virtual mummy excavation, including the subfields of palaeopathology, paleoradiology, cultural and ethnographic context, provenance studies, paleogenetics, and physiological analyses. The template allows lecturers to generate their own versions of the tool for any topic of interest by simply changing the text and pictures. Tests undertaken with students of evolutionary medicine showed that the tool was helpful during their studies. Lecturers commented that they appreciated having a similar tool in other fields. Conclusions and implications: Mummy Explorer fills a gap in the virtual teaching landscape of highly interdisciplinary fields such as evolutionary medicine. It will be offered for free download and can be adapted to any educational topic. Translations into German and possibly other languages are in progress

Quantum discrete Dubrovin equations

The discrete equations of motion for the quantum mappings of KdV type are given in terms of the Sklyanin variables (which are also known as quantum separated variables). Both temporal (discrete-time) evolutions and spatial (along the lattice at a constant time-level) evolutions are considered. In the classical limit, the temporal equations reduce to the (classical) discrete Dubrovin equations as given in a previous publication. The reconstruction of the original dynamical variables in terms of the Sklyanin variables is also achieved.Comment: 25 page

Neuromelanin, neurotransmitter status and brainstem location determine the differential vulnerability of catecholaminergic neurons to mitochondrial DNA deletions

<p>Abstract</p> <p>Background</p> <p>Deletions of the mitochondrial DNA (mtDNA) accumulate to high levels in dopaminergic neurons of the substantia nigra pars compacta (SNc) in normal aging and in patients with Parkinson's disease (PD). Human nigral neurons characteristically contain the pigment neuromelanin (NM), which is believed to alter the cellular redox-status. The impact of neuronal pigmentation, neurotransmitter status and brainstem location on the susceptibility to mtDNA damage remains unclear. We quantified mtDNA deletions (ΔmtDNA) in single pigmented and non-pigmented catecholaminergic, as well as non-catecholaminergic neurons of the human SNc, the ventral tegmental area (VTA) and the locus coeruleus (LC), using laser capture microdissection and single-cell real-time PCR.</p> <p>Results</p> <p>In healthy aged individuals, ΔmtDNA levels were highest in pigmented catecholaminergic neurons (25.2 ± 14.9%), followed by non-pigmented catecholamergic (18.0 ± 11.2%) and non-catecholaminergic neurons (12.3 ± 12.3%; p < 0.001). Within the catecholaminergic population, ΔmtDNA levels were highest in dopaminergic neurons of the SNc (33.9 ± 21.6%) followed by dopaminergic neurons of the VTA (21.9 ± 12.3%) and noradrenergic neurons of the LC (11.1 ± 11.4%; p < 0.001). In PD patients, there was a trend to an elevated mutation load in surviving non-pigmented nigral neurons (27.13 ± 16.73) compared to age-matched controls (19.15 ± 11.06; p = 0.052), but levels where similar in pigmented nigral neurons of PD patients (41.62 ± 19.61) and controls (41.80 ± 22.62).</p> <p>Conclusions</p> <p>Catecholaminergic brainstem neurons are differentially susceptible to mtDNA damage. Pigmented dopaminergic neurons of the SNc show the highest ΔmtDNA levels, possibly explaining the exceptional vulnerability of the nigro-striatal system in PD and aging. Although loss of pigmented noradrenergic LC neurons also is an early feature of PD pathology, mtDNA levels are not elevated in this nucleus in healthy controls. Thus, ΔmtDNA are neither an inevitable consequence of catecholamine metabolism nor a universal explanation for the regional vulnerability seen in PD.</p

First-Year Spectroscopy for the SDSS-II Supernova Survey

This paper presents spectroscopy of supernovae discovered in the first season of the Sloan Digital Sky Survey-II Supernova Survey. This program searches for and measures multi-band light curves of supernovae in the redshift range z = 0.05 - 0.4, complementing existing surveys at lower and higher redshifts. Our goal is to better characterize the supernova population, with a particular focus on SNe Ia, improving their utility as cosmological distance indicators and as probes of dark energy. Our supernova spectroscopy program features rapid-response observations using telescopes of a range of apertures, and provides confirmation of the supernova and host-galaxy types as well as precise redshifts. We describe here the target identification and prioritization, data reduction, redshift measurement, and classification of 129 SNe Ia, 16 spectroscopically probable SNe Ia, 7 SNe Ib/c, and 11 SNe II from the first season. We also describe our efforts to measure and remove the substantial host galaxy contamination existing in the majority of our SN spectra.Comment: Accepted for publication in The Astronomical Journal(47pages, 9 figures