Vive la radiorésistance!: converging research in radiobiology and biogerontology to enhance human radioresistance for deep space exploration and colonization.

While many efforts have been made to pave the way toward human space colonization, little consideration has been given to the methods of protecting spacefarers against harsh cosmic and local radioactive environments and the high costs associated with protection from the deleterious physiological effects of exposure to high-Linear energy transfer (high-LET) radiation. Herein, we lay the foundations of a roadmap toward enhancing human radioresistance for the purposes of deep space colonization and exploration. We outline future research directions toward the goal of enhancing human radioresistance, including upregulation of endogenous repair and radioprotective mechanisms, possible leeways into gene therapy in order to enhance radioresistance via the translation of exogenous and engineered DNA repair and radioprotective mechanisms, the substitution of organic molecules with fortified isoforms, and methods of slowing metabolic activity while preserving cognitive function. We conclude by presenting the known associations between radioresistance and longevity, and articulating the position that enhancing human radioresistance is likely to extend the healthspan of human spacefarers as well

On the non equilibrium thermodynamics and dynamics of a deformable interface between two electro-magnetically controllable fluids

This thesis is devoted to the study of a deformable material interface between two immiscible movingmedia, both of them being magnetizable. The first part concerns the establishment of a complete set of dynamical equations allowing a complete description of the non equilibrium phenomena including a coupling between the internal angular momentum and the electromagnetic field. The effects of the relaxation processes are also discussed. We show that the deformation of the interface introduces a nonlinear term, proportional to the mean curvature, inthe surface dynamical equations of mass momentum and angular momentum. That termintervenes also in the singular magnetic and electric fields inside the interface which lead tothe influence of currents and charge densities at the interface. In a second part, we give the expressionfor the entropy production inside the interface as well as in the bulk phase. Using the general principles of non equilibrium thermodynamics, we compute the different thermodynamical fluxes.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

On the non equilibrium thermodynamics and dynamics of a deformable interface between two electro-magnetically controllable fluids

This thesis is devoted to the study of a deformable material interface between two immiscible movingmedia, both of them being magnetizable. The first part concerns the establishment of a complete set of dynamical equations allowing a complete description of the non equilibrium phenomena including a coupling between the internal angular momentum and the electromagnetic field. The effects of the relaxation processes are also discussed. We show that the deformation of the interface introduces a nonlinear term, proportional to the mean curvature, inthe surface dynamical equations of mass momentum and angular momentum. That termintervenes also in the singular magnetic and electric fields inside the interface which lead tothe influence of currents and charge densities at the interface. In a second part, we give the expressionfor the entropy production inside the interface as well as in the bulk phase. Using the general principles of non equilibrium thermodynamics, we compute the different thermodynamical fluxes.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Adversarial Threshold Neural Computer for Molecular <i>de Novo</i> Design

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the <i>de novo</i> design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp³-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed <i>in vitro</i> validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds

Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

The emergence of the 2019 novel coronavirus (COVID-19), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar, which is far from ideal. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of COVID-19 and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked-based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches were published at www.insilico.com/ncov-sprint/. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. </div

Latest advances in aging research and drug discovery

An increasing aging population poses a significant challenge to societies worldwide. A better understanding of the molecular, cellular, organ, tissue, physiological, psychological, and even sociological changes that occur with aging is needed in order to treat age-associated diseases. The field of aging research is rapidly expanding with multiple advances transpiring in many previously disconnected areas. Several major pharmaceutical, biotechnology, and consumer companies made aging research a priority and are building internal expertise, integrating aging research into traditional business models and exploring new go-to-market strategies. Many of these efforts are spearheaded by the latest advances in artificial intelligence, namely deep learning, including generative and reinforcement learning. To facilitate these trends, the Center for Healthy Aging at the University of Copenhagen and Insilico Medicine are building a community of Key Opinion Leaders (KOLs) in these areas and launched the annual conference series titled "Aging Research and Drug Discovery (ARDD)" held in the capital of the pharmaceutical industry, Basel, Switzerland (www.agingpharma.org). This ARDD collection contains summaries from the 6th annual meeting that explored aging mechanisms and new interventions in age-associated diseases. The 7th annual ARDD exhibition will transpire 2nd-4th of September, 2020, in Basel.Peer reviewe