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Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges

Chemical Biology &Drug Design, Volume 108, Issue 2, August 2026.
This review presents a systems‐oriented roadmap for integrating artificial intelligence into medicinal plant drug discovery to overcome persistent bottlenecks like extract complexity. It highlights that advancing toward reproducible therapeutics requires making phytochemical datasets AI‐ready via rigorous harmonization and phyto‐centric foundational ...
Amit Gangwal, Azim Ansari, Mohd Usman Mohd Siddique, Jyotiram Sawale, Suhas Padmane, Raju Wadekar   +5 more
wiley   +1 more source

GAS-YOLO: a robust soybean seedling detection model trained on single-scene UAV data for complex field environments. [PDF]

Plant Methods
Wu H, Kang J, Yang G, Zhang J, Yang Q, Wang H, Li S, Gao X, Li H.   +8 more
europepmc   +1 more source

GSC-YOLO: A Pedestrian Detection Method for Low-Light Security Surveillance Scenarios. [PDF]

Sensors (Basel)
Qing W, Li F, Li S, Yin P.
europepmc   +1 more source

LHA-YOLO: A Lightweight and High-Accuracy Detector via Parallel Attention and Divide-and-Conquer Fusion for UAV Images. [PDF]

Sensors (Basel)
Yang J, Pan X, Pan Q.
europepmc   +1 more source

Artificial Intelligence-Assisted Colposcopy: Deep Learning Multi-Class Segmentation of Anatomical Structures and Pathological Findings for Cervical Cancer Screening. [PDF]

Cancers (Basel)
Jurczak M, Charzewski Ł, Goźlińska B, Albrycht P, Kobus K, Ludwin A, Jabiry-Zieniewicz Z, Kominek S, Basiński G, Korzeb B, Suchońska BE.   +10 more
europepmc   +1 more source

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YOLO-F: YOLO for Flame Detection

International Journal of Pattern Recognition and Artificial Intelligence, 2023
Flame detection is of great significance in a fire prevention system. YOLOv4 has poor real-time performance on flame detection caused by the complex structure and high parameter size. To address this problem, a novel flame detection framework, YOLO for flame (YOLO-F), is proposed in this paper. The backbone of YOLOv4 is simplified from the original 53
Kun Xu, Yuan Xu, Yuanxin Xing, Zhanwen Liu   +3 more
openaire   +1 more source

BAFPN: An Optimization for YOLO

2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021
Object detection is essential in Computer Vision and is widely applied in all areas. This paper proposes a method called BAFPN. BAFPN is a new bidirectional Feature Pyramid Network that constructs accurate object detection networks based on YOLOv4 by implementing Adaptively Spatial Feature Fusion.
Hehe Li, Xin Li, Lianfeng Li, Junbin Pan, Shujia Zhu, Jinwei Du, Peng Wang   +6 more
openaire   +1 more source

YOLO*C — Adding context improves YOLO performance

Neurocomputing, 2023
You Only Look Once (YOLO) algorithms deliver state-of-the-art performance in object detection. This research proposes a novel one-stage YOLO-based algorithm that explicitly models the spatial context inherent in traffic scenes. The new YOLO*C algorithm introduces the MCTX context module and integrates loss function changes, effectively leveraging rich ...
openaire   +2 more sources

YOLO-FD: YOLO for Face Detection

2019
Face detection is a fundamental step for any face analysis approach. However, it remains as an unsolved problem in computer vision, specially, when it comes to the variability and distractions of in-the-wild environments. Moreover, a face detector must be accurate and fast to be used in surveillance/biometrics scenarios.
Luan P. e Silva, Júlio C. Batista, Olga R. P. Bellon, Luciano Silva   +3 more
openaire   +1 more source