Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning.

Drug-drug interaction (DDI) prediction identifies interactions of drug combinations in which the adverse side effects caused by the physicochemical incompatibility have attracted much attention. Previous studies usually model drug information from single or dual views of the whole drug molecules but ignore the detailed interactions among atoms, which leads to incomplete and noisy information and limits the accuracy of DDI prediction. In this work, we propose a novel dual-view drug representation learning network for DDI prediction ('DSN-DDI'), which employs local and global representation learning modules iteratively and learns drug substructures from the single drug ('intra-view') and the drug pair ('inter-view') simultaneously. Comprehensive evaluations demonstrate that DSN-DDI significantly improved performance on DDI prediction for the existing drugs by achieving a relatively improved accuracy of 13.01% and an over 99% accuracy under the transductive setting. More importantly, DSN-DDI achieves a relatively improved accuracy of 7.07% to unseen drugs and shows the usefulness for real-world DDI applications. Finally, DSN-DDI exhibits good transferability on synergistic drug combination prediction and thus can serve as a generalized framework in the drug discovery field.

R2-DDI: relation-aware feature refinement for drug-drug interaction prediction.

Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

Breaking the barriers of data scarcity in drug-target affinity prediction.

Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.

Functional Composite Dual-Phase In Situ Self-Reconstruction Design for High-Energy-Density Li-Rich Cathodes.

The unique anionic redox mechanism provides, high-capacity, irreversible oxygen release and voltage/capacity degradation to Li-rich cathode materials (LRO, Li1.2Mn0.54Co0.13Ni0.13O2). In this study, an integrated stabilized carbon-rock salt/spinel composite heterostructured layers (C@spinel/MO) is constructed by in situ self-reconstruction, and the generation mechanism of the in situ reconstructed surface is elucidated. The formation of atomic-level connections between the surface-protected phase and bulk-layered phase contributes to electrochemical performance. The best-performing sample shows a high increase (63%) of capacity retention compared to that of the pristine sample after 100 cycles at 1C, with an 86.7% reduction in surface oxygen release shown by differential electrochemical mass spectrometry. Soft X-ray results show that Co3+ and Mn4+ are mainly reduce in the carbothermal reduction reaction and participate in the formation of the spinel/MO rock-salt phase. The results of oxygen release characterized by Differential electrochemical mass spectrometry (DEMS) strongly prove the effectiveness of surface reconstruction.

BioGPT: generative pre-trained transformer for biomedical text generation and mining.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Improved drug-target interaction prediction with intermolecular graph transformer.

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.

SPRoBERTa: protein embedding learning with local fragment modeling.

Well understanding protein function and structure in computational biology helps in the understanding of human beings. To face the limited proteins that are annotated structurally and functionally, the scientific community embraces the self-supervised pre-training methods from large amounts of unlabeled protein sequences for protein embedding learning. However, the protein is usually represented by individual amino acids with limited vocabulary size (e.g. 20 type proteins), without considering the strong local semantics existing in protein sequences. In this work, we propose a novel pre-training modeling approach SPRoBERTa. We first present an unsupervised protein tokenizer to learn protein representations with local fragment pattern. Then, a novel framework for deep pre-training model is introduced to learn protein embeddings. After pre-training, our method can be easily fine-tuned for different protein tasks, including amino acid-level prediction task (e.g. secondary structure prediction), amino acid pair-level prediction task (e.g. contact prediction) and also protein-level prediction task (remote homology prediction, protein function prediction). Experiments show that our approach achieves significant improvements in all tasks and outperforms the previous methods. We also provide detailed ablation studies and analysis for our protein tokenizer and training framework.

Tryptophan catabolism via the kynurenine pathway regulates infection and inflammation: from mechanisms to biomarkers and therapies.

BACKGROUND: L-Tryptophan (L-Trp), an essential amino acid, is the only amino acid whose level is regulated specifically by immune signals. Most proportions of Trp are catabolized via the kynurenine (Kyn) pathway (KP) which has evolved to align the food availability and environmental stimulation with the host pathophysiology and behavior. Especially, the KP plays an indispensable role in balancing the immune activation and tolerance in response to pathogens. SCOPE OF REVIEW: In this review, we elucidate the underlying immunological regulatory network of Trp and its KP-dependent catabolites in the pathophysiological conditions by participating in multiple signaling pathways. Furthermore, the KP-based regulatory roles, biomarkers, and therapeutic strategies in pathologically immune disorders are summarized covering from acute to chronic infection and inflammation. MAJOR CONCLUSIONS: The immunosuppressive effects dominate the functions of KP induced-Trp depletion and KP-produced metabolites during infection and inflammation. However, the extending minor branches from the KP are not confined to the immune tolerance, instead they go forward to various functions according to the specific condition. Nevertheless, persistent efforts should be made before the clinical use of KP-based strategies to monitor and cure infectious and inflammatory diseases.

Targeted inhibition of the PI3K/AKT/mTOR pathway by (+)-anthrabenzoxocinone induces cell cycle arrest, apoptosis, and autophagy in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC), characterized by low survival rates and a high recurrence rate, is a major cause of cancer-related mortality. Aberrant activation of the PI3K/AKT/mTOR signaling pathway is a common driver of NSCLC. Within this study, the inhibitory activity of (+)-anthrabenzoxocinone ((+)-ABX), an oxygenated anthrabenzoxocinone compound derived from Streptomyces, against NSCLC is demonstrated for the first time both in vitro and in vivo. Mechanistically, it is confirmed that the PI3K/AKT/mTOR signaling pathway is targeted and suppressed by (+)-ABX, resulting in the induction of S and G2/M phase arrest, apoptosis, and autophagy in NSCLC cells. Additionally, the augmentation of intracellular ROS levels by (+)-ABX is revealed, further contributing to the inhibition of the signaling pathway and exerting inhibitory effects on tumor growth. The findings presented in this study suggest that (+)-ABX possesses the potential to serve as a lead compound for the treatment of NSCLC.

Mesenchymal stem cell-derived exosomes as a new drug carrier for the treatment of spinal cord injury: A review.

Spinal cord injury (SCI) is a devastating traumatic disease seriously impairing the quality of life in patients. Expectations to allow the hopeless central nervous system to repair itself after injury are unfeasible. Developing new approaches to regenerate the central nervous system is still the priority. Exosomes derived from mesenchymal stem cells (MSC-Exo) have been proven to robustly quench the inflammatory response or oxidative stress and curb neuronal apoptosis and autophagy following SCI, which are the key processes to rescue damaged spinal cord neurons and restore their functions. Nonetheless, MSC-Exo in SCI received scant attention. In this review, we reviewed our previous work and other studies to summarize the roles of MSC-Exo in SCI and its underlying mechanisms. Furthermore, we also focus on the application of exosomes as drug carrier in SCI. In particular, it combs the advantages of exosomes as a drug carrier for SCI, imaging advantages, drug types, loading methods, etc., which provides the latest progress for exosomes in the treatment of SCI, especially drug carrier.

Discovering drug-target interaction knowledge from biomedical literature.

MOTIVATION: The interaction between drugs and targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually triplets about drugs, targets and their interaction, becomes an urgent demand in the industry. Existing methods of discovering biological knowledge are mainly extractive approaches that often require detailed annotations (e.g. all mentions of biological entities, relations between every two entity mentions, etc.). However, it is difficult and costly to obtain sufficient annotations due to the requirement of expert knowledge from biomedical domains. RESULTS: To overcome these difficulties, we explore an end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations. Further, we propose a semi-supervised method, which leverages the aforementioned end-to-end model to filter unlabeled literature and label them. Experimental results show that our method significantly outperforms extractive baselines on DTI discovery. We also create a dataset, KD-DTI, to advance this task and release it to the community. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/bert-nmt/BERT-DTI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics.

Machine learning force fields (MLFFs) have gained popularity in recent years as they provide a cost-effective alternative to ab initio molecular dynamics (MD) simulations. Despite a small error on the test set, MLFFs inherently suffer from generalization and robustness issues during MD simulations. To alleviate these issues, we propose global force metrics and fine-grained metrics from element and conformation aspects to systematically measure MLFFs for every atom and every conformation of molecules. We selected three state-of-the-art MLFFs (ET, NequIP, and ViSNet) and comprehensively evaluated on aspirin, Ac-Ala3-NHMe, and Chignolin MD datasets with the number of atoms ranging from 21 to 166. Driven by the trained MLFFs on these molecules, we performed MD simulations from different initial conformations, analyzed the relationship between the force metrics and the stability of simulation trajectories, and investigated the reason for collapsed simulations. Finally, the performance of MLFFs and the stability of MD simulations can be further improved guided by the proposed force metrics for model training, specifically training MLFF models with these force metrics as loss functions, fine-tuning by reweighting samples in the original dataset, and continued training by recruiting additional unexplored data.

Risk factors associated with loss of variable-thread tapered implants: A retrospective observational study of 1-5 years.

OBJECTIVES: This study aimed to evaluate the survival rate of variable-thread tapered implants (VTTIs) and identify risk factors for early/late implant loss. MATERIALS AND METHODS: From January 2016 to December 2019, patients who received VTTIs were included in this study. The cumulative survival rates (CSRs) at implant/patient levels were calculated by the life table method and presented via Kaplan-Meier survival curves. The relation between investigated variables and early/late implant loss was analyzed by the multivariate generalized estimating equation (GEE) regression model on the implant level. RESULTS: A total of 1528 patients with 2998 VTTIs were included. 95 implants from 76 patients were lost at the end of observation. At the implant level, the CSRs at 1, 3, and 5 years were 98.77%, 96.97%, and 95.39%, respectively, whereas they were 97.84%, 95.31%, and 92.96% at the patient level, respectively. The multivariate analysis revealed that non-submerged implant healing (OR = 4.63, p = .037) was associated with the early loss of VTTIs. Besides, male gender (OR = 2.48, p = .002), periodontitis (OR = 3.25, p = .007), implant length <10 mm (OR = 2.63, p = .028), and overdenture (OR = 9.30, p = .004) could significantly increase the risk of late implant loss. CONCLUSION: Variable-thread tapered implants could reach an acceptable survival rate in clinical practice. Non-submerged implant healing was associated with early implant loss; male gender, periodontitis, implant length <10 mm, and overdenture would significantly increase the risk of late implant loss.

Indicator-based assessments of the coupling coordination degree and correlations of water-energy-food-ecology nexus in Uzbekistan.

Grappling with the global ecological concern of the Aral Sea disaster, Uzbekistan exemplifies the urgent necessity of unravelling and addressing the complex Water-Energy-Food-Ecology (WEFE) nexus conflicts in arid regions, a critical task yet largely uncharted. Through the strategic process of 'Indicator Articulation - Weight Calibration - Nexus Coordination Quantification - Correlational Analysis', this work has developed a tailored framework that integrates a novel, context-specific indicator system, enabling an illumination of the intricate dynamics within the WEFE nexus in arid regions. During 2000-2018, the WEFE Nexus in Uzbekistan showed low-level coordination, indicating systemic imbalances. The Aral Sea crisis was the central disruptor, resulting in a moderately disordered ecological subsystem. Concurrently, disorder was observed in water resources, signaling inadequate management and potential overutilization. Furthermore, Coordination for energy and food were barely coordinated and under primary coordination respectively, underlining critical challenges in energy efficiency and food security. Over the last two decades, the WEFE Nexus has evolved towards a tighter interlinkage, yet the stability of this coupling coordination has experienced increased fluctuations, indicating that Uzbekistan's policies in the WEFE subsystems have been less stable in the last two decades and are in need of further adjustment and improvement. To address the challenges, we recommend a comprehensive approach that integrates technological, infrastructure, and policy solutions is needed. Specifically, promoting water-saving irrigation technology, renewing and maintaining outdated energy facilities, and raising public awareness of ecological protection are part of the essential measures. Furthermore, alleviating the contradiction between economic growth and ecological conservation remains a major challenge. Collectively, our constructed WEFE Nexus framework, with its extendable and context-specific indicators, holds significant potential for broad application in the analysis of multi-sectoral sustainability, particularly within arid regions globally, and forms a solid foundation for the formulation of effective, targeted policies and sustainable development strategies.

Comparative of Forensic DNA Identification Using Cell Lysis Method and Magnetic Beads Method.

OBJECTIVES: To compare the effects of cell lysis method and magnetic beads method in forensic DNA identification and to explore these two methods in forensic DNA identification. METHODS: The genome DNA of THP-1 cells in different quantities was extracted by the cell lysis method and magnetic beads method, and the DNA content was quantified by real-time quantitative PCR. The cell lysis method and magnetic beads method were used to type the STR of human blood with different dilution ratios. RESULTS: When the numbers of THP-1 cell were 100, 400 and 800, the DNA content extracted by cell lysis method were (1.219±0.334), (5.081±0.335), (9.332±0.318) ng, respectively; and the DNA content extracted by magnetic beads method were (1.020±0.281), (3.634±0.482), (7.896±0.759) ng, respectively. When the numbers of THP-1 cells were 400 and 800, the DNA content extracted by the cell lysis method was higher than that by the magnetic beads method. The sensitivity of cell lysis method and magnetic beads method was similar in STR typing of human blood at different dilution ratios. Complete STR typing could be obtained at 100, 300 and 500-fold dilutions of blood samples, but could not be detected at 700-fold dilution. STR typing of undiluted human blood could not be detected by cell lysis method. CONCLUSIONS: The cell lysis method is easy to operate and can retain template DNA to the maximum extend. It is expected to be suitable for trace blood evidence tests.

SAMF: a self-adaptive protein modeling framework.

MOTIVATION: Gradient descent-based protein modeling is a popular protein structure prediction approach that takes as input the predicted inter-residue distances and other necessary constraints and folds protein structures by minimizing protein-specific energy potentials. The constraints from multiple predicted protein properties provide redundant and sometime conflicting information that can trap the optimization process into local minima and impairs the modeling efficiency. RESULTS: To address these issues, we developed a self-adaptive protein modeling framework, SAMF. It eliminates redundancy of constraints and resolves conflicts, folds protein structures in an iterative way, and picks up the best structures by a deep quality analysis system. Without a large amount of complicated domain knowledge and numerous patches as barriers, SAMF achieves the state-of-the-art performance by exploiting the power of cutting-edge techniques of deep learning. SAMF has a modular design and can be easily customized and extended. As the quality of input constraints is ever growing, the superiority of SAMF will be amplified over time. AVAILABILITY AND IMPLEMENTATION: The source code and data for reproducing the results is available at https://msracb.blob.core.windows.net/pub/psp/SAMF.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Arabidopsis HDZIP class II transcription factor ABA INSENSITIVE TO GROWTH 1 functions in leaf development.

Leaf laminar growth and adaxial-abaxial boundary formation are fundamental outcomes of plant development. Boundary and laminar growth coordinate the further patterning and growth of the leaf, directing the differentiation of cell types within the top and bottom domains and promoting initiation of lateral organs along their adaxial or abaxial axis. Leaf adaxial-abaxial polarity specification and laminar outgrowth are regulated by two transcription factors, REVOLUTA (REV) and KANADI (KAN). ABA INSENSITIVE TO GROWTH 1 (ABIG1) encodes a HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIP) class II transcription factor and is a direct target of the adaxial-abaxial regulators REV and KAN. To investigate the role of ABIG1 in leaf development and in the establishment of polarity, we examined the phenotypes of both gain-of-function and loss-of-function mutants. Through genetic interaction analysis with REV and KAN mutants, we determined that ABIG1 plays a role in leaf laminar growth as well as in adaxial-abaxial polarity establishment. Genetic and physical interaction assays showed that ABIG1 interacts with the transcriptional TOPLESS corepressor. This study provides new evidence that ABIG1, another HD-ZIP II, facilitates growth through the corepressor TOPLESS.

Isolation and identification of plant growth-promoting rhizobacteria from tall fescue rhizosphere and their functions under salt stress.

Soil salinity has become one of the major factors that threaten tall fescue growth and turf quality. Plants recruit diverse microorganisms in the rhizosphere to cope with salinity stress. In this study, 15 plant growth-promoting rhizobacteria (PGPR) were isolated from the salt-treated rhizosphere of tall fescue and were annotated to 10 genera, including Agrobacterium, Fictibacillus, Rhizobium, Bhargavaea, Microbacterium, Paenarthrobacter, Pseudarthrobacter, Bacillus, Halomonas, and Paracoccus. All strains could produce indole-3-acetic acid (IAA). Additionally, eight strains exhibited the ability to solubilize phosphate and potassium. Most strains could grow on the medium containing 600 mM NaCl, such as Bacillus zanthoxyli and Bacillus altitudinis. Furthermore, Bacillus zanthoxyli and Bacillus altitudinis were inoculated with tall fescue seeds and seedlings to determine their growth-promoting effect. The results showed that Bacillus altitudinis and mixed culture significantly increased the germination rate of tall fescue seeds. Bacillus zanthoxyli can significantly increase the tillers number and leaf width of seedlings under salt conditions. Through the synergistic effect of FaSOS1, FaHKT1, and FaHAK1 genes, Bacillus zanthoxyli helps to expel the excess Na+ from aboveground parts and absorb more K+ in roots to maintain ion homeostasis in tall fescue. Unexpectedly, we found that Bacillus altitudinis displayed an inapparent growth-promoting effect on seedlings under salt stress. Interestingly, the mixed culture of the two strains was also able to alleviate, to some extent, the effects of salt stress on tall fescue. This study provides a preliminary understanding of tall fescue rhizobacteria and highlights the role of Bacillus zanthoxyli in tall fescue growth and salt tolerance.