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Motivation The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. Results By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. Availability and implementation Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. Results Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug–drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. Availability and implementation Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The heterologous expression of recombinant protein requires host cells, such as Escherichiacoli , and the solubility of protein greatly affects the protein yield. A novel and highly accurate solubility predictor that concurrently improves the production yield and minimizes production cost, and that forecasts protein solubility in an E.coli expression system before the actual experimental work is highly sought. Results In this article, EPSOL, a novel deep learning architecture for the prediction of protein solubility in an E.coli expression system, which automatically obtains comprehensive protein feature representations using multidimensional embedding, is presented. EPSOL outperformed all existing sequence-based solubility predictors and achieved 0.79 in accuracy and 0.58 in Matthew's correlation coefficient. The higher performance of EPSOL permits large-scale screening for sequence variants with enhanced manufacturability and predicts the solubility of new recombinant proteins in an E.coli expression system with greater reliability. Availability and implementation EPSOL's best model and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/EPSOL). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Multiple sequence alignment (MSA) is a fundamental problem in bioinformatics. The quality of alignment will affect downstream analysis. MAFFT has adopted the Fast Fourier Transform method for searching the homologous segments and using them as anchors to divide the sequences, then making alignment only on segments, which can save time and memory without overly reducing the sequence alignment quality. MAFFT becomes slow when the dataset is large. Results We made a software, WMSA, which uses the divide-and-conquer method to split the sequences into clusters, aligns those clusters into profiles with the center star strategy and then makes a progressive profile–profile alignment. The alignment is conducted by the compiled algorithms of MAFFT, K-Band with multithread parallelism. Our method can balance time, space and quality and performs better than MAFFT in test experiments on highly conserved datasets. Availability and implementation Source code is freely available at https://github.com/malabz/WMSA/ , which is implemented in C/C++ and supported on Linux, and datasets are available at https://github.com/malabz/WMSA-dataset. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]