Application of a deep generative model produces novel and diverse functional peptides against microbial resistance.

Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic's world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.

Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets.

Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles information including separation information, chemical equilibrium, chemical reactivity analysis, and solubility modeling. Since, the available ε-prediction models are fairly primitive and frequently suffer from serious failures especially when deals with strong polar compounds. Therefore, we have developed a novel data-driven system to improve the efficiency and wide-range applicability of ε using in material sciences. This innovative scheme adopts the correlation distance and genetic algorithm to discriminate features' combination and avoid overfitting. Herein, the prediction output of the single ML model as a coding to estimate the target value by simulating the layer-by-layer extraction in deep learning, and enabling instant search for the optimal combination of features is recruited. Our model established an improved correlation value of 0.956 with target as compared to the previously available best traditional ML result of 0.877. Our framework established a profound improvement, especially for material systems possessing ε value >50. In terms of interpretability, we have derived a conceptual computational equation from a minimum generating tree. Our innovative data-driven system is preferentially superior over other methods due to its application for the prediction of dielectric constants as well as for the prediction of overall micro and macro-properties of any multi-components complex.

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

In Silico Screening of Potential Spike Glycoprotein Inhibitors of SARS-CoV-2 with Drug Repurposing Strategy.

OBJECTIVE: To select potential molecules that can target viral spike proteins, which may potentially interrupt the interaction between the human angiotension-converting enzyme 2 (ACE2) receptor and viral spike protein by virtual screening. METHODS: The three-dimensional (3D)-coordinate file of the receptor-binding domain (RBD)-ACE2 complex for searching a suitable docking pocket was firstly downloaded and prepared. Secondly, approximately 15,000 molecular candidates were prepared, including US Food and Drug Administration (FDA)-approved drugs from DrugBank and natural compounds from Traditional Chinese Medicine Systems Pharmacology (TCMSP), for the docking process. Then, virtual screening was performed and the binding energy in Autodock Vina was calculated. Finally, the top 20 molecules with high binding energy and their Chinese medicine (CM) herb sources were listed in this paper. RESULTS: It was found that digitoxin, a cardiac glycoside in DrugBank and bisindigotin in TCMSP had the highest docking scores. Interestingly, two of the CM herbs containing the natural compounds that had relatively high binding scores, Forsythiae fructus and Isatidis radix, are components of Lianhua Qingwen (), a CM formula reportedly exerting activity against severe acute respiratory syndrome (SARS)-Cov-2. Moreover, raltegravir, an HIV integrase inhibitor, was found to have a relatively high binding score. CONCLUSIONS: A class of compounds, which are from FDA-approved drugs and CM natural compounds, that had high binding energy with RBD of the viral spike protein. Our work provides potential candidates for other researchers to identify inhibitors to prevent SARS-CoV-2 infection, and highlights the importance of CM and integrative application of CM and Western medicine on treating COVID-19.