Artificial intelligence in the life sciences最新文献

A variety of environmental and physiological conditions can cause oxidative stress that damage cellular components such as DNA, proteins and lipids. Oxidative stress is implicated in many human diseases including cancer, cardiovascular diseases, neurological diseases, inflammatory diseases, and aging. The nuclear factor erythroid 2–related factor 2 (NRF2) is a transcriptional factor that plays a key role in the cellular antioxidant defense system as it regulates transcription of antioxidant proteins and detoxifying enzymes. There is an urgent need to identify novel compounds that activate NRF2 and enhance antioxidant defense. We collected data from the high-throughput screening of NRF2 activators and identified molecular fragments (structural alerts) associated with the activation of NRF2. We also developed ten classification models using different types of molecular descriptors and machine learning techniques. Two approaches were used to establish the applicability domain of developed models: the structure-based approach and the distance to model approach. The best performing model that used message passing neural network (MPNN) technique showed accuracy of 87 % for the test set of chemicals within the distance to model of 0.3. The integrative approach using a combination of generated structural alerts and MPNN model was used to screen approved drugs collected in the DrugBank to identify potential NRF2 activators. Out of 2393 screened chemicals 138 compounds were predicted as NRF2 activators by both approaches. Analysis of these compounds showed that some drugs were already known activators of NRF2 while others are potentially novel activators.

In chemoinformatics, artificial intelligence (AI) continues to grow a symbiosis with open science (OS). Such a close AI-OS interaction brings substantial practical benefits in research, scientific dissemination, and education, to name a few areas. The AI-OS symbiosis can be further enhanced by combining sufficient substantive expertise, mathematical and statistical knowledge, and coding skills. This Viewpoint discusses the benefits of the smooth and productive interaction between AI, OS, and open data. We also present a short list of misconceptions and pitfalls surrounding AI-OS and propose correct responses and behaviors agreed upon by field experts. In addition, we provide suggestions to continue enhancing the positive contributions of the AI-OS symbiosis towards chemoinformatics.

Biomedical knowledge graphs (KGs) hold valuable information regarding biomedical entities such as genes, diseases, biological processes, and drugs. KGs have been successfully employed in challenging biomedical areas such as the identification of pathophysiology mechanisms or drug repurposing. The creation of high-quality KGs typically requires labor-intensive multi-database integration or substantial human expert curation, both of which take time and contribute to the workload of data processing and annotation. Therefore, the use of automatic systems for KG building and maintenance is a prerequisite for the wide uptake and utilization of KGs. Technologies supporting the automated generation and updating of KGs typically make use of Natural Language Processing (NLP), which is optimized for extracting implicit triples described in relevant biomedical text sources. At the core of this challenge is how to improve the accuracy and coverage of the information extraction module by utilizing different models and tools. The emergence of pre-trained large language models (LLMs), such as ChatGPT which has grown in popularity dramatically, has revolutionized the field of NLP, making them a potential candidate to be used in text-based graph creation as well. So far, no previous work has investigated the power of LLMs on the generation of cause-and-effect networks and KGs encoded in Biological Expression Language (BEL). In this paper, we present initial studies towards one-shot BEL relation extraction using two different versions of the Generative Pre-trained Transformer (GPT) models and evaluate its performance by comparing the extracted results to a highly accurate, manually curated BEL KG curated by domain experts.

Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal for the machine learning model since it relies on the model's predictions or model architecture and therefore cannot be transferred to other models. Inspired by research in pedagogy, we here introduce the concept of yoked machine learning where a second machine learning model learns from the data selected by another model. We found that in 48% of the benchmarked combinations, yoked learning performed similar or better than active learning. We analyze distinct cases in which yoked learning can improve active learning performance. In particular, we prototype yoked deep learning (YoDeL) where a classic machine learning model provides data to a deep neural network, thereby mitigating challenges of active deep learning such as slow refitting time per learning iteration and poor performance on small datasets. In summary, we expect the new concept of yoked (deep) learning to provide a competitive option to boost the performance of active learning and benefit from distinct capabilities of multiple machine learning models during data acquisition, training, and deployment.

Recent technological advancements have revolutionized research capabilities across the biological sciences by enabling the collection of large data that provides a broader picture of systems from the cellular to ecosystem level at a more refined resolution. The rapid rate of generating these data has exacerbated bottlenecks in study design and data analysis approaches, especially as conventional methods that incorporate traditional statistical tests and assumptions are not suitable or sufficient for highly dimensional data (i.e., more than 1,000 variables). The application of machine learning techniques in large data analysis is one promising solution that is increasingly popular. However, limitations in expertise such that the results from machine learning models can be interpreted to gain meaningful biological insight pose a great challenge. To address this challenge, a user-friendly machine learning workflow that can be applied to a wide variety of data types to reduce these large data to those variables (attributes) most determinant of experimental and/or observed conditions is provided, as well as a general overview of data analysis and machine learning approaches and considerations thereof. The workflow presented here has been beta-tested with great success and is recommended to be incorporated into analysis pipelines of large data as a standardized approach to reduce data dimensionality. Moreover, the workflow is flexible, and the underlying concepts and steps can be modified to best suit user needs, objectives, and study parameters.

The accuracy of machine learning models for protein-ligand binding affinity prediction depends on the quality of the experimental data they are trained on. Most of these models are trained and tested on different subsets of the PDBbind database, which is the main source of protein-ligand complexes with annotated binding affinity in the public domain. However, estimating its experimental uncertainty is not straightforward because just a few protein-ligand complexes have more than one measurement associated. In this work, we analyze bioactivity data from ChEMBL to estimate the experimental uncertainty associated with the three binding affinity measures included in the PDBbind (K_i, K_d, and IC₅₀), as well as the effect of combining them. The experimental uncertainty of combining these three affinity measures was characterized by a mean absolute error of 0.78 logarithmic units, a root mean square error of 1.04 and a Pearson correlation coefficient of 0.76. These estimations were contrasted with the performances obtained by state-of-the-art machine learning models for binding affinity prediction, showing that these models tend to be overoptimistic when evaluated on the core set from PDBbind.

Computational approaches have revolutionized the field of drug discovery, collectively known as Computer-Assisted Drug Design (CADD). Advancements in computing power, data generation, digitalization, and artificial intelligence (AI) techniques have played a crucial role in the rise of CADD. These approaches offer numerous benefits, enabling the analysis and interpretation of vast amounts of data from diverse sources, such as genomics, structural information, and clinical trials data. By integrating and analyzing these multiple data sources, researchers can efficiently identify potential drug targets and develop new drug candidates. Among the AI techniques, machine learning (ML) and deep learning (DL) have shown tremendous promise in drug discovery. ML and DL models can effectively utilize experimental data to accurately predict the efficacy and safety of drug candidates. However, despite these advancements, certain areas in drug discovery face data scarcity, particularly in neglected, rare, and emerging viral diseases. Few-shot learning (FSL) is an emerging approach that addresses the challenge of limited data in drug discovery. FSL enables ML models to learn from a small number of examples of a new task, achieving commendable performance by leveraging knowledge learned from related datasets or prior information. It often involves meta-learning, which trains a model to learn how to learn from few data. This ability to quickly adapt to new tasks with low data circumvents the need for extensive training on large datasets. By enabling efficient learning from a small amount of data, few-shot learning has the potential to accelerate the drug discovery process and enhance the success rate of drug development. In this review, we introduce the concept of few-shot learning and its application in drug discovery. Furthermore, we demonstrate the valuable application of few-shot learning in the identification of new drug targets, accurate prediction of drug efficacy, and the design of novel compounds possessing desired biological properties. This comprehensive review draws upon numerous papers from the literature to provide extensive insights into the effectiveness and potential of few-shot learning in these critical areas of drug discovery and development.