Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery.

Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific protein, solubility, or ease of synthesis. We apply the proposed model to generate a novel inhibitor of Janus kinase 3, implicated in rheumatoid arthritis, psoriasis, and vitiligo. The discovered molecule was tested in vitro and showed good activity and selectivity.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Human-specific endogenous retroviral insert serves as an enhancer for the schizophrenia-linked gene PRODH.

Using a systematic, whole-genome analysis of enhancer activity of human-specific endogenous retroviral inserts (hsERVs), we identified an element, hsERVPRODH, that acts as a tissue-specific enhancer for the PRODH gene, which is required for proper CNS functioning. PRODH is one of the candidate genes for susceptibility to schizophrenia and other neurological disorders. It codes for a proline dehydrogenase enzyme, which catalyses the first step of proline catabolism and most likely is involved in neuromediator synthesis in the CNS. We investigated the mechanisms that regulate hsERVPRODH enhancer activity. We showed that the hsERVPRODH enhancer and the internal CpG island of PRODH synergistically activate its promoter. The enhancer activity of hsERVPRODH is regulated by methylation, and in an undermethylated state it can up-regulate PRODH expression in the hippocampus. The mechanism of hsERVPRODH enhancer activity involves the binding of the transcription factor SOX2, whch is preferentially expressed in hippocampus. We propose that the interaction of hsERVPRODH and PRODH may have contributed to human CNS evolution.

Exhaustive data mining comparison of the effects of low doses of ionizing radiation, formaldehyde and dioxins.

BACKGROUND: Ionizing radiation in low doses is the ubiquitous environmental factor with harmful stochastic effects. Formaldehyde is one of the most reactive household and industrial pollutants. Dioxins are persistent organic pollutants and most potent synthetic poisons effective even at trace concentrations. Environmental pollutants are capable of altering the expression of a variety of genes. To identify the similarities and differences in the effects of low-dose ionizing radiation, formaldehyde and dioxin on gene expression, we performed the bioinformatic analysis of all available published data. RESULTS: We found that that in addition to the common p53-, ATM- and MAPK-signaling stress response pathways, genes of cell cycle regulation and proinflammatory cytokines, the studied pollutants induce a variety of other molecular processes. CONCLUSIONS: The observed patterns provide new insights into the mechanisms of the adverse effects associated with these pollutants. They can also be useful in the development of new bio-sensing methods for detection of pollutants in the environment and combating the deleterious effects.

Molecular aspects of development and regulation of endometriosis.

Endometriosis is a common and painful condition affecting women of reproductive age. While the underlying pathophysiology is still largely unknown, much advancement has been made in understanding the progression of the disease. In recent years, a great deal of research has focused on non-invasive diagnostic tools, such as biomarkers, as well as identification of potential therapeutic targets. In this article, we will review the etiology and cellular mechanisms associated with endometriosis as well as the current diagnostic tools and therapies. We will then discuss the more recent genomic and proteomic studies and how these data may guide development of novel diagnostics and therapeutics. The current diagnostic tools are invasive and current therapies primarily treat the symptoms of endometriosis. Optimally, the advancement of "-omic" data will facilitate the development of non-invasive diagnostic biomarkers as well as therapeutics that target the pathophysiology of the disease and halt, or even reverse, progression. However, the amount of data generated by these types of studies is vast and bioinformatics analysis, such as we present here, will be critical to identification of appropriate targets for further study.

Application of Spiroplasma melliferum proteogenomic profiling for the discovery of virulence factors and pathogenicity mechanisms in host-associated spiroplasmas.

To date, no genome of any of the species from the genus Spiroplasma has been completely sequenced. Long repetitive sequences similar to mobile units present a major obstacle for current genome sequencing technologies. Here, we report the assembly of the Spiroplasma melliferum KC3 genome into 4 contigs, followed by proteogenomic annotation and metabolic reconstruction based on the discovery of 521 expressed proteins and comprehensive metabolomic profiling. A systems approach allowed us to elucidate putative pathogenicity mechanisms and to discover major virulence factors, such as Chitinase utilization enzymes and toxins never before reported for insect pathogenic spiroplasmas.

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine.

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics - An AI-Enabled Biological Target Discovery Platform.

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

Evaluation of the geroprotective effects of withaferin A in Drosophila melanogaster.

Withanolides are a class of compounds usually found in plant extracts which are an attractive geroprotective drug design starting point. We evaluated the geroprotective properties of Withaferin A (WA) in vivo using the Drosophila model. Flies were supplemented by nutrient medium with WA (at a concentration of 1, 10, or 100 µM dissolved in ethanol) for the experiment group and 30 µM of ethanol for the control group. WA treatment at 10 and 100 µM concentrations prolong the median life span of D. melanogaster's male by 7.7, 9.6% (respectively) and the maximum life span (the age of death 90% of individuals) by 11.1% both. Also WA treatment at 1, 10 and 100 µM improved the intestinal barrier permeability in older flies and affected an expression of genes involved in antioxidant defense (PrxV), recognition of DNA damage (Gadd45), heat shock proteins (Hsp68, Hsp83), and repair of double-strand breaks (Ku80). WA was also shown to have a multidirectional effect on the resistance of flies to the prooxidant paraquat (oxidative stress) and 33° C hyperthermia (heat shock). WA treatment increased the resistance to oxidative stress in males at 4 and 7 week old and decreased it at 6 weeks old. It increased the male's resistance to hyperthermia at 2, 4 and 7 weeks old and decreased it at 3, 5 and 8 weeks old. WA treatment decreased the resistance to hyperthermia in females at 1, 2 and 3 weeks old and not affected on their resistance to oxidative stress.

Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders.

Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model-the Bidirectional Adversarial Autoencoder-explicitly separates cellular processes captured in gene expression changes into two feature sets: those related and unrelated to the drug incubation. The model uses related features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE.

Erratum: Addendum: Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders.

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Radioprotectors.org: an open database of known and predicted radioprotectors.

The search for radioprotectors is an ambitious goal with many practical applications. Particularly, the improvement of human radioresistance for space is an important task, which comes into view with the recent successes in the space industry. Currently, all radioprotective drugs can be divided into two large groups differing in their effectiveness depending on the type of exposure. The first of these is radioprotectors, highly effective for pulsed, and some types of relatively short exposure to irradiation. The second group consists of long-acting radioprotectors. These drugs are effective for prolonged and fractionated irradiation. They also protect against impulse exposure to ionizing radiation, but to a lesser extent than short-acting radioprotectors. Creating a database on radioprotectors is a necessity dictated by the modern development of science and technology. We have created an open database, Radioprotectors.org, containing an up-to-date list of substances with proven radioprotective properties. All radioprotectors are annotated with relevant chemical and biological information, including transcriptomic data, and can be filtered according to their properties. Additionally, the performed transcriptomics analysis has revealed specific transcriptomic profiles of radioprotectors, which should facilitate the search for potent radioprotectors.

Replicative and radiation-induced aging: a comparison of gene expression profiles.

All living organisms are subject to the aging process and experience the effect of ionizing radiation throughout their life. There have been a number of studies that linked ionizing radiation process to accelerated aging, but comprehensive signalome analysis of both processes was rarely conducted. Here we present a comparative signaling pathway based analysis of the transcriptomes of fibroblasts irradiated with different doses of ionizing radiation, replicatively aged fibroblasts and fibroblasts collected from young, middle age and old patients. We demonstrate a significant concordance between irradiation-induced and replicative senescence signalome signatures of fibroblasts. Additionally, significant differences in transcriptional response were also observed between fibroblasts irradiated with high and low dose. Our data shows that the transcriptome of replicatively aged fibroblasts is more similar to the transcriptome of the cells irradiated with 2 Gy, than with 5 сGy.This work revealed a number of signaling pathways that are shared between senescence and irradiation processes and can potentially be targeted by the new generation of gero- and radioprotectors.

Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers.

There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels. We further used those models to quantify the acceleration of biological aging due to tobacco use. Female smokers were predicted to be twice as old as their chronological age compared to nonsmokers, whereas male smokers were predicted to be one and a half times as old as their chronological age compared to nonsmokers. Our findings suggest that deep learning analysis of routine blood tests could complement or even replace the current error-prone method of self-reporting of smoking status and could be expanded to assess the effect of other lifestyle and environmental factors on aging.

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare.

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.

Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations.

Accurate and physiologically meaningful biomarkers for human aging are key to assessing antiaging therapies. Given ethnic differences in health, diet, lifestyle, behavior, environmental exposures, and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here, we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean, and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population specific hematologic aging clocks. The performance of models was also evaluated on publicly available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population specific and combined hematological clocks and all-cause mortality. Overall, this study suggests (a) the population specificity of aging patterns and (b) hematologic clocks predicts all-cause mortality. The proposed models were added to the freely-available Aging.AI system expanding the range of tools for analysis of human aging.

Aging and drug discovery.

Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age-associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcare.

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.

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.

Towards natural mimetics of metformin and rapamycin.

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.