Leveraging Transformer Models for Accelerated Drug Discovery

In the realm of AI-accelerated drug discovery, particularly in de novo drug design, significant challenges include unpredictable drug responses in clinical trials, biases in predictive models, and the opaque nature of AI methodologies that complicate the understanding of a drug's mechanism of action. These issues have limited the progression of AI-discovered drugs into clinical trials and regulatory approval. Concurrently, the development of me-too drugs, which involve modifications of existing drugs within the same therapeutic class, presents a less risky and potentially more effective avenue. However, the potential of AI to enhance their development remains largely underexplored. This dissertation aims to transform the development of me-too drugs through the application of AI, with a focus on transformer and large language models (LLMs). It introduces innovative frameworks that utilize the representation learning and generative capabilities of transformer models to refine and expedite the me-too drug development process. These methodologies, referred to as "drug optimization", seek to further accelerate the production of effective me-too drugs. This work makes four significant contributions to the field: (1) It proposes two fusion methods that integrate transformer models with graph neural networks, enhancing the precision of binding affinity predictions. (2) It assembles a comprehensive dataset of 10 million binding affinity values across a diverse array of proteins and drugs, providing an invaluable resource for model training and validation. (3) It proposes two generative models for drug optimization, fine-tuned through reinforcement learning, with the goal of automating and expediting the creation of effective me-too drugs. (4) It introduces an innovative bidirectional GPT model for molecular textual sequences (SMILES), enabling precise generative mask infilling for targeted drug optimization. And by conducting comprehensive evaluations on real world viral and cancer target proteins, we demonstrate that the proposed drug optimization frameworks can consistently enhance existing molecules/drugs

Feature Weaken: Vicinal Data Augmentation for Classification

Deep learning usually relies on training large-scale data samples to achieve better performance. However, over-fitting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continuously. For the same purpose, we subversively propose a novel training method, Feature Weaken, which can be regarded as a data augmentation method. Feature Weaken constructs the vicinal data distribution with the same cosine similarity for model training by weakening features of the original samples. In especially, Feature Weaken changes the spatial distribution of samples, adjusts sample boundaries, and reduces the gradient optimization value of back-propagation. This work can not only improve the classification performance and generalization of the model, but also stabilize the model training and accelerate the model convergence. We conduct extensive experiments on classical deep convolution neural models with five common image classification datasets and the Bert model with four common text classification datasets. Compared with the classical models or the generalization improvement methods, such as Dropout, Mixup, Cutout, and CutMix, Feature Weaken shows good compatibility and performance. We also use adversarial samples to perform the robustness experiments, and the results show that Feature Weaken is effective in improving the robustness of the model.Comment: 9 pages,6 figure

Knowledge and Attitudes Towards Obesity and Bariatric Surgery in Chinese Nurses

Abstract Background Obesity has become a global epidemic. Surgical treatment of obesity and metabolic disorders in China is increasing rapidly, but it is still a new discipline even to health professionals. As an important member of the multidisciplinary team, the knowledge and attitudes of nurses provide crucial health care to the patients and support to surgeons. Objectives To study the Chinese nurses' knowledge of obesity and metabolic disorders, and attitudes towards bariatric surgery and to improve their capability of work in this new discipline. Methods This is a multicenter study, with the questionnaire distributed to cooperative hospitals in the form of an electronic questionnaire by the First Affiliated Hospital of Jinan University in April 2018. A questionnaire was designed to investigate nurses' demographic, knowledge, and attitude towards obesity, weight loss, and bariatric surgery. Results A total of 5311 questionnaires were received, with an effective rate of 91.8% (4878 questionnaires); 65.2% of nurses had a normal BMI. Nurses generally had a high knowledge of obesity and related cardiovascular diseases (98.6%) and type 2 diabetes mellitus (90.2%). However, there was a lack of knowledge in other related aspects, for example its relations to carcinoma (49.5%), gastroesophageal reflux disease (40.1%), and psychological disorders (49.1%), which are controversial issues in bariatric surgery. It was found that education (p < 0.05) had an important influence to nurses' knowledge about the comorbidities of obesity. Female nurses had a higher tendency to choose weight loss than males, but male nurses did physical exercise more frequently than females (p < 0.05). Their acceptance of safety (25.1%) and efficacy (22.9%) of bariatric surgery is low, with concerns predominantly about postoperative complications and adverse effects. Surgical nurses had a more optimistic attitude towards surgery (p < 0.05). Conclusions Chinese nurses have poor knowledge of obesity-related metabolic disorders and also have poor acceptance of surgical treatment modalities. Our findings suggest that it is crucial to enhance the continuing education of Chinese nurses for obesity, metabolic disorders, and bariatric surgery

Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District

Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firstly mapped the location of the maize field by using a remote sensing/phenological–based vegetation classifier and then quantified the maize water use and yield by using a dual-source remote-sensing evapotranspiration (ET) model and a crop water production function, respectively. Validation results show that the adopted phenological-based vegetation classifier performed well in mapping the spatial distributions and inter-annual variations of maize planting, with a kappa coefficient of 0.86. In addition, the ET model based on the hybrid dual-source scheme and trapezoid framework also obtained high accuracy in spatiotemporal ET mapping, with an RMSE of 0.52 mm/day at the site scale and 26.21 mm/year during the maize growing season (April–October) at the regional scale. Further, the adopted crop water production function showed high accuracy in estimating the maize yield, with a mean relative error of only 4.3%. Using the estimated ET, transpiration, and yield of maize, the mean maize WUE based on ET and transpiration in the study region were1.94 kg/m3 and 3.06 kg/m3, respectively. Our results demonstrate the usefulness and validity of remote sensing information in mapping regional crop WUE

Modeling Regional Soil Water Balance in Farmland of the Middle Reaches of Heihe River Basin

Quantifying components of soil water balance in farmland of the middle reaches of Heihe River Basin is essential for efficiently scheduling and allocating limited water resources for irrigation in this arid region. A soil water balance model based on empirical assumptions in the vadose zone of farmland was developed and simulation results were compared/validated with results by the numerical model HYDRUS-1D. Results showed a good coherence between the simulated results of the water balance models and the HYDRUS-1D model in soil water storage, evapotranspiration, deep percolation and groundwater recharge, which indicated that the water balance model was suitable for simulating soil water movement in the study area. Considering the spatial distribution of cropping patterns, groundwater depth and agricultural management, ArcGIS was applied for the pre-/post-processing of the water balance model to quantify the spatial distribution of components of soil water balance in the major cropland in middle reaches of Heihe River Basin. Then, distributions of components of soil water balance in the major cropland under different water-saving irrigation practices during the growing season were predicted and discussed. Simulation results demonstrated that evapotranspiration of the main crops would be more prominently influenced by irrigation quota under deep groundwater depth than that under shallow groundwater depth. Groundwater recharge would increase with the increase of irrigation quota and decrease with the increase of groundwater depth. In general, when groundwater depth reached 3 m, groundwater recharge from root zone was negligible for spring wheat. While when it reached 6 m, groundwater recharge was negligible for maize. Water-saving irrigation practices would help to reduce groundwater recharge with a slight decrease of crop water consumption

Developing an Ensemble Precipitation Algorithm from Satellite Products and Its Topographical and Seasonal Evaluations Over Pakistan

Accurate estimation of precipitation is critical for hydrological, meteorological, and climate models. This study evaluates the performance of satellite-based precipitation products (SPPs) including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B43-v7), Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network (PERSIANN), and PERSIANN-CDR (Climate Data Record), over Pakistan based on Surface Precipitation Gauges (SPGs) at spatial and temporal scales. A novel ensemble precipitation (EP) algorithm is developed by selecting the two best SPPs using the Paired Sample t-test and Principal Component Analysis (PCA). The SPPs and EP algorithm are evaluated over five climate zones (ranging from glacial Zone-A to hyper-arid Zone-E) based on six statistical metrics. The result indicated that IMERG outperformed all other SPPs, but still has considerable overestimation in the highly elevated zones (+20.93 mm/month in Zone-A) and relatively small underestimation in the arid zone (&#8722;2.85 mm/month in Zone-E). Based on the seasonal evaluation, IMERG and TMPA overestimated precipitation during pre-monsoon and monsoon seasons while underestimating precipitation during the post-monsoon and winter seasons. However, the developed EP algorithm significantly reduced the errors both on spatial and temporal scales. The only limitation of the EP algorithm is relatively poor performance at high elevation as compared to low elevations