Accurately predicting molecular properties can identify more promising drug candidates and facilitate the process of drug discovery. There are many advances in methods for molecular property prediction using self-supervised learning. However, most methods tend to focus only on a certain representation, resulting in reduced information diversity. Moreover, less attention is paid to the functional groups in the molecule, which are highly related to the molecular properties. To solve the problem, a multimodal contrastive learning framework called MMCL for molecular property prediction is proposed, which learns common features among similar molecules by utilizing different views of molecules for predicting molecular properties. In addition, by explicitly representing functional groups as nodes in the molecular graph, the model is prompted to learn features related to molecular properties. Prediction results on benchmark datasets and a series of case studies show that MMCL has optimal performance in most property prediction tasks and can capture information related to chemical semantics. MMCL can be used as an efficient deep learning tool for molecular-related tasks to facilitate the drug discovery process.

Drug-target interaction (DTI) is paramount in drug discovery and repurposing, which involves screening for effective candidate drugs by targeting specific proteins. Existing methods often focus on one or two representations of drugs or targets, and little has been explored regarding 3D structures. Moreover, how to capture interactions between multi-modal features comprehensively is also a key issue. A multi-modal interaction fusion method called GraphCF is proposed to overcome these limitations. Specifically, GraphCF uses a MixHop aggregator to gather higher-order neighborhood information between nodes in the DTI topological network and incorporate graph contrastive learning to capture more discriminative 2D representations of drugs and targets. Additionally, GraphCF utilizes convolutional neural networks and graph neural networks to extract the sequence and 3D structural features of drugs and targets, respectively. Then, GraphCF employs a cross-attention-based multi-feature fusion module to facilitate information interaction and fusion among multi-modal feature representations. GraphCF is evaluated and compared with some advanced methods on four public datasets, and the results demonstrate the competitive performance of GraphCF in DTI prediction.

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

How to find the critical nodes in the network structure quickly and accurately is a topic of network science. Various algorithms for critical nodes already exist, of which, however, some are with high time complexity and the rest are limited in application range. To solve this problem, an algorithm, referred to as Vital Node Searcher (VNS), is proposed, which discovers critical nodes from a network based on deep reinforcement learning. The VNS method first takes advantage of the Graph Embedding to downscale the feature information of the target network, and then uses the deep Q network method to extract the critical node sequence. A Long-Short Term network module is designed and applied to fully exploit historical information that is contained in the sequence data. Moreover, a duelling Q network module is developed to enhance the precision of prediction. Both in terms of time complexity and performance, the VNS method is superior compared with other methods, which are validated by experiments of real world datasets. Moreover, VNS method has strong generalisation performance and can be applied to different types of critical node problems. The VNS method performed experiments on four datasets and obtained ANC scores that outperformed the other models respectively. The experiment results demonstrated that the VNS method had a stable and effective performance on finding out the critical node sequence.

Deep reinforcement learning has achieved great success in many fields. However, the agent may get trapped during the exploration, lingering around feckless states that pull the agent away from optimal policies. Thus it's worth studying how to improve learning strategies by forecasting future states. To solve the problem, an algorithm that predicts behaviour consequences before taking action, referred to as Thinking about Action Consequence (TAC), based on the combination of the current state and the policy, is proposed. Deep reinforcement learning approaches equipped with TAC take advantage of the intrinsic prediction framework of TAC as well as bad consequence experiences to determine whether to continue exploring the current region or to evade the potential undesired state discriminated by TAC and instead turn to explore other areas. Since TAC will modify the future plan according to the execution of the policy and the changes of the environment, considering the consequences enables the agent to carry out a suitable exploration mechanism, leading to better performance. Compared with traditional Deep Q Learning (DQN), Double DQN, Dueling DQN, Prioritised Experience Replay DQN, and Intrinsic Reward (IR) methods with uncertain stimulus exploration, our TAC method shows better performance in Atari and Box2d environments.

With the widespread implementation of next-generation sequencing (NGS) technologies, millions of sequences have been produced. A lot of databases were created to store and organize the high-throughput sequencing data. Numerous analysis software programs and tools have been developed over the past years. Most of them use specific formats for data representation and storage. Data interoperability becomes a crucial challenge and many tools have been developed to convert NGS data from one format to another. However, most of them were developed for specific and limited formats. Here, we present NGS-FC (Next-Generation Sequencing Format Converter), which provides a framework to support the conversion between several formats. It supports 14 formats now and provides interfaces to enable users to improve the existing converters and add new ones. Moreover, NGS-FC achieved the overall competitive performance in comparison with some existing converters in terms of RAM usage and running time. The software is written in Java and can be executed standalone. The source code and documentation are freely available at http://sysbio.suda.edu.cn/NGS-FC.

As a notoriously lethal human disease, cancer has obtained much concern for a long time. There have accumulated huge amounts of literature and experimental data on cancer-related research. It is impossible for people to deal with these texts manually to discover novel information and knowledge. However, text mining has an advantage of extracting previously unknown and understandable knowledge from large amounts of texts, and forming well-defined knowledge, providing the possibility to fully taking use of the existed texts. With the proceeding of biomedical research, people have gradually realized that complex biological functions and the phenomenon of life are the results of complex interactions among a variety of biological entities, such as protein. Deeply studying protein interaction network is essential to understand life. We, adopting reinforcement learning idea, put forward an algorithm for protein interaction network constructing. With the algorithm, nodes are used to represent proteins and edges denote interactions. During the evolutionary process, a node selects with which nodes in the network it tends to interact. Keep selecting and carrying on iteration, until eventually attaining an optimal network. The network is the result of the dynamic nature of learning behavior. As a malignancy, prostate cancer has been concerned for a long time. We attain biological texts from PubMed and establish a prostate cancer protein interaction networks by the proposed methods. The results show that our proposed method is pretty good. Network topology analysis results also show that the network node degree distribution is scale-free.