Prediction of protein-ligand binding affinity (PLA) is a crucial problem in drug discovery. Recently, deep learning (DL) models have emerged as a promising and computationally efficient paradigm for the PLA prediction task, enabling rapid and scalable analysis while circumventing the time-consuming nature of experimental assays and the rigidity of conventional scoring functions. However, a significant domain knowledge gap often prohibits the effective integration of biological and computational insights, making it challenging to design deep learning models that comprehensively capture all relevant aspects. Training such models remains a complex undertaking involving multiple facets, including data heterogeneity, model interpretability, and biological plausibility. This review explores the key considerations for training DL models in PLA prediction task, including the choice of datasets, data processing techniques, model architecture design, model training strategies, and evaluation methodologies. Additionally, we discuss the potential applications of PLA prediction in traditional drug discovery and emerging areas, along with the challenges that currently hinder the optimal utilization of deep learning models in this field. This review aims to bridge the gap between computational biology and deep learning by providing a comprehensive guide for researchers interested in leveraging deep learning for PLA prediction.

Precipitation nowcasting is crucial for economic development and social life. Numerous deep learning models have recently been developed and have achieved better results than traditional extrapolation models. However, they mainly focus on improving model architectures, ignoring the impact of error accumulation and data inconsistency. This article proposes a multimodal fusion model named multimodal fusion recurrent neural network (MMF-RNN) for precipitation prediction. Specifically, we use a dual-branch encoder to extract features from radar and ground station data and then fuse them effectively through attention mechanisms and multimodal loss (ML). To address the error accumulation problem, we propose a block-based dynamic weighted loss (BDWLoss) that enables the model to focus more on hard-to-predict areas during training to reduce error accumulation. Based on BDWLoss, we propose an ML that encourages the model to maintain consistency between single-modal and fused multimodal features. In addition, MMF-RNN is compatible with various RNN models such as ConvLSTM, PredRNN, PredRNN++, and MIM. The experimental results on the RAIN-F dataset demonstrate that MMF-RNN outperforms both the single-modal model MS-RNN and the multimodal model MM-RNN. In particular, MMF-RNN achieves significant improvement in predicting heavy precipitation. Compared to MM-PredRNN++, MMF-PredRNN++ shows marked improvements across various performance metrics, with critical success index (CSI) ($R\geq 5 $) and Heidke skill score (HSS) ($R\geq 5$) increasing by 58.08% and 48.55%, respectively, and CSI ($R\geq 10$) and HSS ($R\geq 10$) showing more pronounced gains. These advancements are facilitated not only by the proposed architectural innovations but also by sample weighting, which collectively contribute to superior performance on imbalanced precipitation datasets.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Background. Code-line-level bugginess identification (CLBI) is a vital technique that can facilitate developers to identify buggy lines without expending a large amount of human effort. Most of the existing studies tried to mine the characteristics of source codes to train supervised prediction models, which have been reported to be able to discriminate buggy code lines amongst others in a target program. Problem. However, several simple and clear code characteristics, such as complexity of code lines, have been disregarded in the current literature. Such characteristics can be acquired and applied easily in an unsupervised way to conduct more accurate CLBI, which also can decrease the application cost of existing CLBI approaches by a large margin. Objective. We aim at investigating the status quo in the field of CLBI from the perspective of (1) how far we have really come in the literature, and (2) how far we have yet to go in the industry, by analyzing the performance of state-of-the-art (SOTA) CLBI approaches and tools, respectively. Method. We propose a simple heuristic baseline solution GLANCE (aimin G at contro L - AN d C ompl E x-statements) with three implementations (i.e., GLANCE-MD, GLANCE-EA, and GLANCE-LR). GLANCE is a two-stage CLBI framework: first, use a simple model to predict the potentially defective files; second, leverage simple code characteristics to identify buggy code lines in the predicted defective files. We use GLANCE as the baseline to investigate the effectiveness of the SOTA CLBI approaches, including natural language processing (NLP) based, model interpretation techniques (MIT) based, and popular static analysis tools (SAT). Result. Based on 19 open-source projects with 142 different releases, the experimental results show that GLANCE framework has a prediction performance comparable or even superior to the existing SOTA CLBI approaches and tools in terms of 8 different performance indicators. Conclusion. The results caution us that, if the identification performance is the goal, the real progress in CLBI is not being achieved as it might have been envisaged in the literature and there is still a long way to go to really promote the effectiveness of static analysis tools in industry. In addition, we suggest using GLANCE as a baseline in future studies to demonstrate the usefulness of any newly proposed CLBI approach.

In practice, the data owners of source projects may need to share data without disclosing sensitive information. Therefore, privacy-preserving data-sharing becomes an important topic in cross-company defect prediction (CCDP). In this context, the challenge is how to achieve a high privacy-preserving level while ensuring the utility of the shared privatized data for CCDP. CLIFF&MORPH is a recently proposed state-of-the-art privacy-preserving data-sharing algorithm for CCDP. It has been reported that the CLIFF&MORPH CCDP model produces a promising defect prediction performance. However, we find that ManualDown, a simple (unsupervised) module size model, built on the target projects has a comparable or even better defect prediction performance. Since ManualDown does not require any source project data to build the model, it is free of the privacy-preserving data-sharing challenges for CCDP. This means that, for practitioners, the motivation of applying privacy-preserving data-sharing algorithms to CCDP could not be well justified if the utility challenge is not addressed. We analyze the implications of our findings and outline the directions for future research. In particular, we strongly suggest that future studies at least use ManualDown as a baseline model for comparison to help develop practical privacy-preserving data-sharing algorithms for CCDP.

A complete description of trust relationship is key to construct a high precision trust model. But most of existing models not only miss the negative information and the hesitation information of trust, but also ignore the discordance between text comments and ratings. To solve the problems, a dynamic trust evaluation model based on the affect intensity is proposed. In the model, the intensity of sentimental polarities are calculated from words in comments. The corresponding relation between evaluations of trust property and the emotional intensity vector can be also described. Time series weights to distinguish the importance of transaction at different time are determined by the inverse form of exponential distribution. So the dynamic attenuation of trust can be described. To improve the polymerization ability of trust information, the local trust, the feedback trust and the overall trust are calculated by operators of fuzzy logic. The experimental results show the proposed model can effectively describe the trust relationship between nodes to identify and eliminate various malicious attacks significantly. Copyright © 2016 John Wiley & Sons, Ltd.