arXiv subjects

Jintao Zhu

Publications and source records attributed to Jintao Zhu.

7 recordsLinked to original sources

arXiv2026

BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical semantics distributed across text, tables, and figures, but also reconstructing chemically exact ligand structures (e.g., Markush structures). To address this bottleneck, we introduce BioMiner, a multi-modal extraction framework that explicitly separates bioactivity semantic interpretation from ligand structure construction. Within BioMiner, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm, in which multi-modal large language models operate on chemically grounded visual representations to infer inter-structure relationships, and exact molecular construction is delegated to domain chemistry tools. For rigorous evaluation and method development, we further establish BioVista, a comprehensive benchmark comprising 16,457 bioactivity entries curated from 500 publications. BioMiner validates its extraction ability and provides a quantitative baseline, achieving an F1 score of 0.32 for bioactivity triplets. BioMiner's practical utility is demonstrated via three applications: (1) extracting 82,262 data from 11,683 papers to build a pre-training database that improves downstream models performance by 3.9%; (2) enabling a human-in-the-loop workflow that doubles the number of high-quality NLRP3 bioactivity data, helping 38.6% improvement over 28 QSAR models and identification of 16 hit candidates with novel scaffolds; and (3) accelerating protein-ligand complex bioactivity annotation, achieving a 5.59-fold speed increase and 5.75% accuracy improvement over manual workflows in PoseBusters dataset.

cs.AI↗

arXiv2024

Research on Composite Bit Technology for Hard Formations and Its Application in Igneous Rock

The igneous rocks in deep formation have the characteristics of hardness, poor drillability and high abrasiveness, which is a difficulty in speeding up drilling. The drilling efficiency of existing conventional bits is low in igneous rocks. Based on the characteristics of igneous rocks, rock mechanical parameters and drillability experiments of granite, sandstone and other rocks were carried out. The rock drilling experiments of composite bit, tri-cone bit and PDC bit were carried out. Experiments have shown that in granite with very high strength, the drilling efficiency of conventional cone bit is very low, and it is extremely difficult for PDC bit to penetrate. The impact crushing effect of the cone of the composite bit can make the rock at the bottom of the well produce pits and cracks, which can assist the PDC cutters to penetrate into the formation, and solve the problem of the PDC cutters difficulty in penetrating in hard formations. In softer formations, the rock-breaking advantage of composite bit is not obvious, and the rock-breaking efficiency is lower than that of PDC bit. However, in hard formations, the advantage of composite bit is obvious, with higher drilling efficiency than PDC bit and cone bits. The personalized composite bit developed for deep igneous rocks formations has fast drilling speed, strong sustained drilling ability, long footage, and significant drilling speed-up effect. It significantly reduces the number of runs in deep drilling operations and achieves good application results. The composite bit is suitable for drilling in deep igneous hard-to-drill formations, and it has obvious advantages in deep igneous formations. It is a good choice for drilling speed-up in this kind of hard-to-drill formation.

physics.app-ph↗

arXiv2024

DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking

Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leveraging geometric deep learning (GDL), have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. Despite these advancements, current methods are often tailored for specific docking settings, and limitations such as the neglect of protein side-chain structures, difficulties in handling large binding pockets, and challenges in predicting physically valid structures exist. To accommodate various docking settings and achieve accurate, efficient, and physically reliable docking, we propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking. We innovatively reframe the pocket prediction task as a pocket-ligand alignment problem rather than direct prediction in the first stage. Then we follow a bi-level coarse-to-fine iterative refinement process to perform site-specific docking. Comprehensive experiments demonstrate the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieves a 31\% relative improvement over the docking success rate compared with the previous state-of-the-art GDL model. With the consideration of physical validity, this improvement increases to about 300\%.

q-bio.BM↗

arXiv2024

DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction

Protein (receptor)--ligand interaction prediction is a critical component in computer-aided drug design, significantly influencing molecular docking and virtual screening processes. Despite the development of numerous scoring functions in recent years, particularly those employing machine learning, accurately and efficiently predicting binding affinities for protein--ligand complexes remains a formidable challenge. Most contemporary methods are tailored for specific tasks, such as binding affinity prediction, binding pose prediction, or virtual screening, often failing to encompass all aspects. In this study, we put forward DeepRLI, a novel protein--ligand interaction prediction architecture. It encodes each protein--ligand complex into a fully connected graph, retaining the integrity of the topological and spatial structure, and leverages the improved graph transformer layers with cosine envelope as the central module of the neural network, thus exhibiting superior scoring power. In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously. For the latter two objectives, we augment the dataset through a docking procedure, incorporate suitable physics-informed blocks and employ an effective contrastive learning approach. Eventually, our model manifests a balanced performance across scoring, ranking, docking, and screening, thereby demonstrating its ability to handle a range of tasks. Overall, this research contributes a multi-objective framework for universal protein--ligand interaction prediction, augmenting the landscape of structure-based drug design.

q-bio.BM↗

arXiv2023

DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

q-bio.BM↗

arXiv2022

MolMiner: You only look once for chemical structure recognition

Molecular structures are always depicted as 2D printed form in scientific documents like journal papers and patents. However, these 2D depictions are not machine-readable. Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades follow a rule-based approach where the key step of vectorization of the depiction is based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software MolMiner, which is primarily built up using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with distance-based construction algorithm. We carefully evaluate our software on four benchmark datasets with the state-of-the-art performance. Various real application scenarios are also tested, yielding satisfactory outcomes. The free download links of Mac and Windows versions are available: Mac: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/mac/PharmaMind-mac-latest-setup.dmg and Windows: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/win/PharmaMind-win-latest-setup.exe

cs.CV↗

arXiv2020

Angular Trapping of Spherical Janus Particles

Developing angular trapping methods, which will enable optical tweezers to rotate a micronized bead, is of great importance for the studies of biomacromolecules during a wide range of torque-generation processes. Here we report a novel controlled angular trapping method based on composite Janus particles. We used a chemically synthesized Janus particle, which consists of two hemispheres made of polystyrene (PS) and poly(methyl methacrylate) (PMMA) respectively, as a model system to demonstrate this method. Through computational and experimental studies, we demonstrated the feasibility to control the rotation of a Janus particle in a linearly polarized laser trap. Our results showed that the Janus particle aligned its two hemisphere's interface parallel to the laser propagation direction as well as the laser polarization direction. In our experiments, the rotational state of the particle can be easily and directly visualized by using a CMOS camera, and does not require complex optical detection system. The rotation of the Janus particle in the laser trap can be fully controlled in real time by controlling the laser polarization direction. Our newly developed angular trapping technique has the great advantage of easy implementation and real time controllability. Considering the easy chemical synthesis of Janus particles and implementation of the angular trapping, this novel method has the potential of becoming a general angular trapping method. We anticipate that this new method will significantly broaden the availability of angular trapping in the biophysics community, and expand the scope of the research that can be enabled by the angular trapping approach.

physics.optics↗