Opinions of individuals in large groups evolve through interactions with neighbors and the environment, which can be modeled with opinion dynamics. In this paper, we propose a distributed opinion-based strategy for large-scale multi-robot task allocation utilizing the convergence behaviors of opinion dynamics. The strategy relies on the specialized opinion dynamics on the unit sphere for robot task selection. We investigate the convergence behaviors of opinion dynamics in the context of regions of attraction. Simulation results with a swarm of 200 homogeneous robots validate the effectiveness of our proposed strategy.

High-performance molecular property prediction is one of the essential problems in many research fields. Although Deep Learning models (DLMs) have the strong potential to predict molecular property, the search space of their neural structure and hyperparameters is too huge to enumerate all possibilities, which implies that one will be difficult to get a optimal configuration solution, or even build a DLM with poor performance. To tackle the aforementioned problem, we first abstractly map various types of supervised DLMs for chemical molecular property prediction under a unified molecular DLM conceptualization. Then, we develop a self-learning molecular DLMs construction and optimization framework, called GEP-DL4Mol, based on the unified supervised molecular DLM conceptualization and Gene Expression Programming. Experiments on eight benchmark datasets including 19 property prediction tasks were conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method outperforms other methods.

Distributed Data Fusion (DDF), as a prevalent technique that empowers scalable, flexible, and robust information fusing, has been employed in various multi-sensor networks operating in uncertain and dynamic environments. This paper proposes a cognition difference-based mechanism to construct a dynamic trust network for real-time DDF, where the cognition difference is defined as the statistical difference between the sensors' estimated probability distributions. Distinguished by the mutual correlation between trust and cognition difference, two principles of determining trust are investigated, and their performances are analyzed by conducting simulations in the scenarios of source seeking. Our simulation and experiment results show that the proposed approach is effective in providing comprehensive and robust performance in general and unstructured environments.

Molecular property prediction (MPP) is a crucial and fundamental task for AI-aided drug discovery (AIDD). Recent studies have shown great promise of applying self-supervised learning (SSL) to producing molecular representations to cope with the widely-concerned data scarcity problem in AIDD. As some specific substructures of molecules play important roles in determining molecular properties, molecular representations learned by deep learning models are expected to attach more importance to such substructures implicitly or explicitly to achieve better predictive performance. However, few SSL pre-trained models for MPP in the literature have ever focused on such substructures. To challenge this situation, this paper presents a Chemistry-Aware Fragmentation for Effective MPP (CAFE-MPP in short) under the self-supervised contrastive learning framework. First, a novel fragment-based molecular graph (FMG) is designed to represent the topological relationship between chemistry-aware substructures that constitute a molecule. Then, with well-designed hard negative pairs, a is pre-trained on fragment-level by contrastive learning to extract representations for the nodes in FMGs. Finally, a Graphormer model is leveraged to produce molecular representations for MPP based on the embeddings of fragments. Experiments on 11 benchmark datasets show that the proposed CAFE-MPP method achieves state-of-the-art performance on 7 of the 11 datasets and the second-best performance on 3 datasets, compared with six remarkable self-supervised methods. Further investigations also demonstrate that CAFE-MPP can learn to embed molecules into representations implicitly containing the information of fragments highly correlated to molecular properties, and can alleviate the over-smoothing problem of graph neural networks.

MOTIVATION: Contrastive learning has been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, existing methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. RESULTS: To address this problem, in this article, we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models. AVAILABILITY AND IMPLEMENTATION: The code is publicly available at https://github.com/ZiqiaoZhang/FraSICL.

MOTIVATION: Accurately predicting drug-target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement. One challenge in building deep learning models for DTI prediction is how to appropriately represent drugs and targets. Target distance map and molecular graph are low dimensional and informative representations, which however have not been jointly used in DTI prediction. Another challenge is how to effectively model the mutual impact between drugs and targets. Though attention mechanism has been used to capture the one-way impact of targets on drugs or vice versa, the mutual impact between drugs and targets has not yet been explored, which is very important in predicting their interactions. RESULTS: Therefore, in this article we propose MINN-DTI, a new model for DTI prediction. MINN-DTI combines an interacting-transformer module (called Interformer) with an improved Communicative Message Passing Neural Network (CMPNN) (called Inter-CMPNN) to better capture the two-way impact between drugs and targets, which are represented by molecular graph and distance map, respectively. The proposed method obtains better performance than the state-of-the-art methods on three benchmark datasets: DUD-E, human and BindingDB. MINN-DTI also provides good interpretability by assigning larger weights to the amino acids and atoms that contribute more to the interactions between drugs and targets. AVAILABILITY AND IMPLEMENTATION: The data and code of this study are available at https://github.com/admislf/MINN-DTI.

MOTIVATION: Molecular property prediction is a hot topic in recent years. Existing graph-based models ignore the hierarchical structures of molecules. According to the knowledge of chemistry and pharmacy, the functional groups of molecules are closely related to its physio-chemical properties and binding affinities. So, it should be helpful to represent molecular graphs by fragments that contain functional groups for molecular property prediction. RESULTS: In this article, to boost the performance of molecule property prediction, we first propose a definition of molecule graph fragments that may be or contain functional groups, which are relevant to molecular properties, then develop a fragment-oriented multi-scale graph attention network for molecular property prediction, which is called FraGAT. Experiments on several widely used benchmarks are conducted to evaluate FraGAT. Experimental results show that FraGAT achieves state-of-the-art predictive performance in most cases. Furthermore, our case studies show that when the fragments used to represent the molecule graphs contain functional groups, the model can make better predictions. This conforms to our expectation and demonstrates the interpretability of the proposed model. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available in GitHub, at https://github.com/ZiqiaoZhang/FraGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrated a serial special data processing methods, a bidirectional gated recurrent unit-based RNN (BiGRU) and a fully connected neural network for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.