Artificial intelligence in the life sciences最新文献

The mechanism of action (MoA) of a compound describes the biological interaction through which it produces a pharmacological effect. Multiple data sources can be used for the purpose of predicting MoA, including compound structural information, and various assays, such as those based on cell morphology, transcriptomics and metabolomics. In the present study we explored the benefits and potential additive/synergistic effects of combining structural information, in the form of Morgan fingerprints, and morphological information, in the form of five-channel Cell Painting image data. For a set of 10 well represented MoA classes, we compared the performance of deep learning models trained on the two datasets separately versus a model trained on both datasets simultaneously. On a held-out test set we obtained a macro-averaged F1 score of 0.58 when training on only the structural data, 0.81 when training on only the image data, and 0.92 when training on both together. Thus indicating clear additive/synergistic effects and highlighting the benefit of integrating multiple data sources for MoA prediction.

One of the common strategies to identify novel chemical matter in drug discovery consists in performing a High Throughput Screening (HTS). However, the large amount of data generated at the dose-response (DR) step of an HTS campaign requires a careful analysis to detect artifacts and correct erroneous datapoints before validating the experiments. This step which requires to review each DR experiment can be time consuming and prone to human errors or inconsistencies. AI4DR is a system that has been developed for the classification of DR curves based on a Convolutional Neural Network (CNN) acting on normalized images of the DR curves. AI4DR allows the annotation in minutes of thousands of curves among 14 categories to help the High Throughput Screening biologists in their analyses. Several categories are associated with active and inactive compounds, other categories correspond to features of interest such as the presence of noise, a weaker effect at high doses, or a suspiciously weak or strong slope at the inflexion point of the DR curves of actives. The classifier has been trained on an algorithmically generated dataset curated and refined by experts, tested using real screening campaigns and improved using thousands of annotations by experts. The solution is deployed using a MLFlow model server interfaced with the Genedata Screener data analysis software used by the end users. AI4DR improves the consistency, the robustness, and the speed of HTS data analysis as well as reducing the human effort to identify faster new medicines for patients.

Recent advances in the field of artificial intelligence, specifically regarding deep learning methods, have invigorated research into novel ways for the exploration of chemical space. Compared to more traditional methods that rely on chemical fragments and combinatorial recombination deep generative models generate molecules in a non-transparent way that defies easy rationalization. However, this opaque nature also promises to explore uncharted chemical space in novel ways that do not rely on structural similarity directly. These aspects and the complexity of training such models makes model assessment regarding novelty, uniqueness, and distribution of generated molecules a central aspect. This perspective gives an overview of current methodologies for chemical space exploration with an emphasis on deep neural network approaches. Key aspects of generative models include choice of molecular representation, the targeted chemical space, and the methodology for assessing and validating chemical space coverage.

Artificial intelligence is becoming an important diagnostic and prognostic tool in recent years, as machine learning algorithms have been shown to improve clinical decision-making. These algorithms will have some of their most important applications in developing regions with restricted data collection, but their performance under this condition is still widely unknown. We analyzed longitudinal data from São Paulo, Brazil, to develop machine learning algorithms to predict the risk of death in patients with cancer. We tested different algorithms using nine separate model structures. Considering the area under the ROC curve (AUC-ROC), we obtained values of 0.946 for the general model, 0.945 for the model with the five main cancers, 0.899 for bronchial and lung cancer, 0.947 for breast cancer, 0.866 for stomach cancer, 0.872 for colon cancer, 0.923 for rectum cancer, 0.955 for prostate cancer, and 0.917 for uterine cervix cancer. Our results indicate the potential of building models for predicting mortality risk in cancer patients in developing regions using only routinely-collected data.

One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.

Ontologies are used to support access to a multitude of databases that cover domains relevant information. Heterogeneity and different semantics can be accessed by using structured texts and descriptions in a hierarchical concept definition. We are interested in Life Sciences (LS) related ontologies including components taken from molecular biology, bioinformatics, physics, chemistry, medicine and other related areas. An Ontology comprises: (i) term connections, (ii) the identification of core concepts, (iii) data management, (iv) knowledge classification and integration to collect key information. An ontology may be very useful in navigating through LS terms. This paper explores some available biomedical ontologies and frameworks. It describes the most common ontology development environments (ODE): Protégé, Topbraid Composer, Ontostudio, Fluent Editor, VocBench, Swoop and Obo-edit, to create ontologies from textual scientific resources for LS plans. It also compares ontology methodologies in terms of Usability, Scalability, Stability, Integration, Documentation and Originality.

The A_2A adenosine receptor (A_2AR) is emerging as a promising drug target for cancer immunotherapy. Novel A_2AR antagonists are highly demanded due to few candidates entering clinic trials specific for cancer treatment. Structure-based virtual screening has made a great contribution to discover novel A_2AR antagonists, but most depended on inefficient molecular docking on relatively small molecular databases. In this work, a deep learning strategy was applied to accelerate docking-based virtual screening, through which new structural types of A_2AR antagonists for an extremely large molecular library were found successfully.

The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), artificial potential field (APF), simulated annealing (SA), ant colony optimization (ACO), artificial bee colony algorithm (ABC), harmony search algorithm (HS), bat algorithm (BA), cell decomposition (CD) and firefly algorithm (FA)) in agriculture, focusing on expert systems, robots developed for agriculture, sensors technology for collecting and transmitting data, in an attempt to reveal their potential impact in the field of agriculture. None of the literature highlights the application of AI techniques and robots in (Cultivation, Monitoring, and Harvesting) to understand their contribution to the agriculture sector and the simultaneous comparison of each based on its usefulness and popularity. This work investigates the comparative analysis of three essential phases of agriculture: Cultivation, Monitoring, and Harvesting, by knowing the depth of AI involved and the robots utilized. The current study presents a systematic review of more than 150 papers based on the existing automation application in agriculture from 1960 to 2021. It highlights the future research gap in making intelligent autonomous systems in agriculture. The paper concludes with tabular data and charts comparing the frequency of individual AI approaches for specific applications in the agriculture field.

Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry. Herein, we highlight the opportunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main challenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further.