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Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC 50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target–cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure–activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold. [ABSTRACT FROM AUTHOR]

The use of passive acoustic monitoring (PAM) can compensate for the shortcomings of traditional survey methods on spatial and temporal scales and achieve all-weather and wide-scale assessment and prediction of environmental dynamics. Assessing the impact of human activities on biodiversity by analyzing the characteristics of acoustic scenes in the environment is a frontier hotspot in urban forestry. However, with the accumulation of monitoring data, the selection and parameter setting of the deep learning model greatly affect the content and efficiency of sound scene classification. This study compared and evaluated the performance of different deep learning models for acoustic scene classification based on the recorded sound data from Guangzhou urban forest. There are seven categories of acoustic scenes for classification: human sound, insect sound, bird sound, bird–human sound, insect–human sound, bird–insect sound, and silence. A dataset containing seven acoustic scenes was constructed, with 1000 samples for each scene. The requirements of the deep learning models on the training data volume and training epochs in the acoustic scene classification were evaluated through several sets of comparison experiments, and it was found that the models were able to achieve satisfactory accuracy when the training sample data volume for a single category was 600 and the training epochs were 100. To evaluate the generalization performance of different models to new data, a small test dataset was constructed, and multiple trained models were used to make predictions on the test dataset. All experimental results showed that the DenseNet_BC_34 model performs best among the comparison models, with an overall accuracy of 93.81% for the seven acoustic scenes on the validation dataset. This study provides practical experience for the application of deep learning techniques in urban sound monitoring and provides new perspectives and technical support for further exploring the relationship between human activities and biodiversity. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic bird sound recognition using artificial intelligence technology has been widely used to identify bird species recently. However, the bird sounds recorded in the wild are usually mixed sounds, which can affect the accuracy of identification. In this paper, we utilized massive amounts of data of bird sounds and proposed an efficient time-domain single-channel bird sound separation network. Our proposed network achieved good separation performance and fast separation speed while greatly reducing the consumption of computational resources. Our work may help to discriminate individual birds and study the interaction between individual birds, as well as to realize the automatic identification of bird species in various mobile and edge computing devices. Bird sounds have obvious characteristics per species, and they are an important way for birds to communicate and transmit information. However, the recorded bird sounds in the field are usually mixed, which making it challenging to identify different bird species and to perform associated tasks. In this study, based on the supervised learning framework, we propose a bird sound separation network, a dual-path tiny transformer network, to directly perform end-to-end mixed species bird sound separation in the time-domain. This separation network is mainly composed of the dual-path network and the simplified transformer structure, which greatly reduces the computational resources required of the network. Experimental results show that our proposed separation network has good separation performance (SI-SNRi reaches 19.3 dB and SDRi reaches 20.1 dB), but compared with DPRNN and DPTNet, its parameters and floating point operations are greatly reduced, which means a higher separation efficiency and faster separation speed. The good separation performance and high separation efficiency indicate that our proposed separation network is valuable for distinguishing individual birds and studying the interaction between individual birds, as well as for realizing the automatic identification of bird species on a variety of mobile devices or edge computing devices. [ABSTRACT FROM AUTHOR]

Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited in their application because of their low efficiency, unsuitability for non-stationary noise, generally unimproved signal-to-noise ratio (SNR) efficacy, and considerable amounts of residual noise. These limitations hinder the effective processing of recorded signals for which extraneous noise overlaps with bird vocalizations. In this study, we propose a bioacoustic noise reduction method based on a deep feature loss network for bird sounds. The method has a rapid denoising speed and can more effectively remove background noise from field recording signals without distorting the bird acoustic spectrum. The denoising effects of the proposed method were compared with those of a speech enhancement generative adversarial network, web real-time communications denoising, and other noise reduction methods. The denoising ability of these methods for different noises was evaluated using spectrograms and objective evaluation measures such as the SNR and perceptual evaluation of speech quality (PESQ). The experimental results revealed that our proposed noise reduction method can obtain higher SNRs and PESQ scores than other noise reduction methods, with the SNR increasing by up to 35.83 dB following denoising. • In open environments, acoustic data collected using acoustic sensor can be polluted. • Existing bioacoustics noise reduction methods are often limited. • We propose a bioacoustics noise reduction method for bird sounds. • Our method has a faster denoising speed and is more effective. • This noise reduction method can obtain higher SNR and PESQ. [ABSTRACT FROM AUTHOR]

• A novel end-to-end deep learning model, named CicadaNet is proposed for filtering cicada chorus from recordings containing bird sound • Our proposed algorithm achieves the best performance over current state-of-the-art deep denoising models and traditional denoising algorithms • A clean bird sound dataset is built and a large amount of real cicada noise data is collected for model evaluation Passive acoustic monitoring has been an effective tool for bird sound analysis. However, bird sounds often include cicada noise, which is an obstacle for investigating bird sounds. For example, cicada noise can result in large deviations of acoustic index, which will lead to the mismonitoring of species richness trends. Therefore, there is a critical need to filter cicada noise for helping bird sound analysis. We develop a novel end-to-end deep learning model, named CicadaNet for filtering cicada chorus from recordings containing bird sound. CicadaNet utilizes a convolutional encoder-decoder network to encode and decode acoustic features and a conformer module for global and local sequence modeling. We build a clean bird sound dataset and collect a large amount of real cicada noise data for model evaluation. We compare CicadaNet with current state-of-the-art deep denoising models and traditional denoising algorithms. Experimental results show that CicadaNet achieves the best denoising performance (SegSNR is improved by 9.59 dB and SI-SNR is improved by 20.08 dB when the noisy SNR = 0 dB). Meanwhile, CicadaNet achieves good performance for the real-time denoising of cicada noise. Furthermore, CicadaNet achieves bird species-independent noise reduction. We evaluate the effectiveness of CicadaNet for bird diversity survey. CicadaNet achieves the best performance, which can effectively eliminate the deviation caused by cicada noise to the acoustic index. CicadaNet can be easily extended to the cancellation of other environmental noise, and we propose it for the acoustic denoising of other vocalizing animals. [ABSTRACT FROM AUTHOR]

• A ResNet based automated bio-acoustics presence detection frame-work was proposed. • SpecAugment and label smoothing are effective in bio-acoustics presence detection task. • An automatic post-processing method was proposed by employing the persistence characteristics. As the most precious primate with only about 30 left, it is highly desirable to monitor the population of Hainan gibbon for the sake of effective protection. The traditional monitoring technique is facing the challenges of statistical hysteresis and a large number of labor costs, which makes it unsustainable. Deep learning based passive acoustic monitoring has shown its great potential in automatic detection of Hainan gibbon calls in Dufourq et al. (2021). Motivated by this progress, a bioacoustics presence detection network (BPDnet) was proposed for Hainan gibbon calls by tailoring acoustic signal pre-processing, feature extraction, label smoothing loss function, as well as data augmentation in the ResNet-based passive acoustic detection framework. In addition, a post-processing procedure was proposed to further improve the passive acoustic presence detection. Numerical results are presented to show that, the proposed BPDnet outperforms the baseline scheme of Dufourq et al. (2021) in terms of bio-acoustics presence detection accuracy, callback rate, F 1 -score by at least 12.9% when using the same 72-h test recording and test method without manual intervention. When manual post-processing is allowed, the proposed BPDnet provides us an almost perfect Hainan gibbon call presence detection scheme. [ABSTRACT FROM AUTHOR]