Artificial intelligence in the life sciences最新文献

The Structure-Activity Relationship (SAR) Matrix (SARM) method systematically extracts structurally related compound series from any source and organizes these series in a unique data structure formed by matrices similar to R-group tables from medicinal chemistry. In addition, the SARM method generates virtual analogues for structurally organized series that consist of new combinations of existing core structures and R-groups. For active compounds, SARMs visualize SAR patterns and aid in compound design. The SARM methodology and data structure was integrated with a recurrent neural network architecture to further expand the compound design capacity with deep generative models, leading to the DeepSARM approach. Herein, we present an extension of the DeepSARM framework for compound optimization termed iterative DeepSARM (iDeepSARM), which involves multiple iterations of deep generative modeling and fine-tuning to obtain increasingly likely active compounds for targets of interest. Hence, iDeepSARM adds computational hit-to-lead and lead optimization capability to the DeepSARM framework. In addition to detailing methodological features and calculation protocols, an exemplary compound design application is reported to illustrate the iDeepSARM approach.

Aqueous solubility is a key chemical property that drives various processes in chemistry and biology. Its computational prediction is challenging, as evidenced by the fact that it has been a subject of considerable interest for several decades. Recent work has explored fingerprint-based, feature-based and graph-based representations with different machine learning and deep learning methodologies. In general, many traditional methods have been proposed, but they rely heavily on the quality of the rule-based, hand-crafted features. On the other hand, limitations in the quality of aqueous solubility data become a handicap when training deep models. In this study, we have developed a novel structure-aware method for the prediction of aqueous solubility by introducing a new deep network architecture and then employing a transfer learning approach. The model was proven to be competitive, obtaining an RMSE of 0.587 during both cross-validation and a test on an independent dataset. To be more precise, the method is evaluated on molecules downloaded from the Online Chemical Database and Modeling Environment (OCHEM). Beyond aqueous solubility prediction, the strategy presented in this work may be useful for modeling any kind of (chemical or biological) properties for which there is a limited amount of data available for model training.

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

OmicInt is an R software package developed for a user-friendly and in-depth exploration of significantly changed genes, gene expression patterns, and the associated epigenetic features as well as the related miRNA environment. In addition, OmicInt offers single cell RNA-seq and proteomics data integration to elucidate specific expression profiles. To achieve this, OmicInt builds on a novel scoring function capturing expression and pathology associations. The developed scoring function together with the implemented Gaussian mixture modelling pipline helps to explore genes and the linked interactome networks. The machine learning pipeline was designed to make the analyses straightforward for the non-experts so that researchers could take advantage of advanced analytics for their data evaluation. Additional functionalities, such as protein type and cellular location classification, provide useful assessments of the key interactors. The introduced package can aid in studying specific gene networks, understanding cellular perturbation events, and exploring interactions that might not be easily detectable otherwise. Thus, this robust set of bioinformatics tools can be very beneficial in drug discovery and target evaluation. OmicInt is designed to be freely accessible to involve a larger bioinformatics community and continuously improve the developed algorithmic methods.

Accurate molecular property or activity prediction is one of the main goals in computer-aided drug design. Quantitative structure-activity relationship (QSAR) modeling and machine learning, more recently deep learning, have become an integral part of this process. Such algorithms require lots of data for training which, in the case of physico-chemical and bioactivity data sets, remains scarce. To address the lack of data, augmentation techniques are increasingly applied in deep learning. Here, we exploit that one compound can be represented by various SMILES strings as means of data augmentation and we explore several augmentation techniques. Convolutional and recurrent neural networks are trained on four data sets, including experimental solubility, lipophilicity, and bioactivity measurements. Moreover, the uncertainty of the models is assessed by applying augmentation on the test set. Our results show that data augmentation improves the accuracy independently of the deep learning model and of the size of the data. The best strategies lead to the Maxsmi models, the models that maximize the performance in SMILES augmentation. Our findings show that the standard deviation of the per SMILES prediction correlates with the accuracy of the associated compound prediction. In addition, our systematic testing of different augmentation strategies provides an extensive guideline to SMILES augmentation. A prediction tool using the Maxsmi models for novel compounds on the aforementioned physico-chemical and bioactivity tasks is made available at https://github.com/volkamerlab/maxsmi.

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. Deep Learning has been widely adopted in speech and image recognition, natural language processing which has an impact on healthcare. In the recent decade, the application of DL has exponentially grown in the field of Ophthalmology. The fundoscopy, slit lamp photography, optical coherence tomography (OCT), and magnetic resonance imaging (MRI) were employed for clinical examination of various ocular conditions. These data served as a perfect platform for the development of DL models in Ophthalmology. Currently, the application of DL in ocular disorders is majorly studied in Diabetic retinopathy (DR), age-related macular degeneration (AMD), macular oedema, retinopathy of prematurity (ROP), glaucoma, and cataract. In Ophthalmology, DL models are gradually expanding their scope in optic neuropathies. Glaucoma and optic neuritis are optic nerve disorders, where DL models are currently studied for clinical applications. For further expansion of DL application in inherited optic neuropathies, we discussed the recent observational studies revealing the pathophysiological changes at the optic nerve in Leber's hereditary optic neuropathy (LHON). LHON is an inherited optic neuropathy leading to bilateral loss of vision in early age groups. Hence for early management, further footsteps in the application of DL in LHON will benefit both ophthalmologists and patients. In this review, we discuss the recent advancements of AI in the Ophthalmology and prospective of applying DL models in LHON for clinical precision and timely diagnosis.

Despite the burgeoning development of artificial intelligence (AI) applied in the medical field, there have been little bibliometric and collaboration network researches on the patents related to this inter-disciplinary research domain. Patentometric and Social Network Analysis (SNA) are used to conduct the characterizations of patent applications and cooperative networks, mapping a holistic landscape related to the AI-medical field. Derwent Innovation Index database (DII) is adopted as the patent data source. The results indicate that the quantity of AI-medical-related patent applications has been increasing explosively since 2011. The United States of America (US) is both the foremost country developing related technologies and the primary target of patent filing by non-residents. The hotspot of the current research include medical image recognition, computer-aided diagnosis, disease monitoring, disease prediction, bioinformatics, and drug development, etc. Low density of the assignees cooperation network implies the slight patent collaboration. Companies and academic institutions are the friskiest innovation subjects in the AI-medical field. The geographical proximity has a positive influence on the patent collaboration because co-owned patents are concentrated on the institutes in the same nation. Domestic collaboration is the major collaborative pattern. The spatial agglomeration of trans-regional patent cooperation is fairly sparse, which requires a further escalation in knowledge circulation. It has practical significance to understand the developing situation and patent cooperation network in the AI-medical field, providing a reference for future strategy planning, development, and technological marketization.

The objective of this article is to review the application of informatics-driven approaches in the pharmacovigilance field with focus on drug-drug interaction (DDI) safety signal discovery using various data sources. Signal can be a new safety information or new aspect to already known adverse drug reaction which is possibly causally related to a medication/medications that warrants further investigation to accept or refute. Signals can be detected from different data sources such as spontaneous reporting system, scientific literature, biomedical databases and electronic health records. This review is substantiated based on the fact that DDIs are contributing to a public health problem represented in 6-30% adverse drug event occurrences. In this article, we review informatics-driven approaches applied by authors focusing on DDI signal detection using different data sources. The aim of this article is not to laboriously survey all PV literature. As an alternative, we discussed informatics-driven methods used to discover DDI signals and various data sources reinforced with instances of studies from PV literature. The adoption of informatics-driven approaches can complement and optimize the practice of safety signal detection. However, further researches should be carried out to evaluate the efficiency of those approaches and to address the limitations of external validation, implementation and adoption in real clinical environments and by the regulatory bodies.