Skip to content
Log in

Author

Yingchao Yan

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

PAM-DB: Revealing Protein Activation Mechanisms for Next-Generation Rational Drug Discovery

Current rational drug design relies predominantly on computational (CADD/AIDD) methods that model binding thermodynamics and static conformations of target proteins, primarily in their inactive states. However, the kinetic parameters that govern experimental efficacy—such as catalytic turnover and signaling potency—are determined by molecular interactions with transition states (TS), intermediate states (IS), and the entire continuum of conformations along the least free-energy activation pathway. The absence of this dynamic dimension has fundamentally limited the predictive power and success rate of conventional structure-based approaches. Here, we present a structural database that systematically maps the complete activation trajectories of pharmaceutically relevant targets, encompassing TS, IS, and all connecting conformational ensembles. This resource offers multiple strategic advantages for drug discovery: enabling rational targeting of previously “undruggable” proteins, facilitating biased agonism/antagonism design, revealing cryptic allosteric sites in inactive conformations, identifying novel transient pockets along the activation route, rationalizing the mechanisms of existing drugs, predicting mutational effects on activation barriers, and prospectively forecasting drug resistance and off-target liabilities. We demonstrate the utility of this database through representative case studies and provide implementation guidelines for integration into existing discovery pipelines. More detailed information can be found at our website: https://www.momedpamdb.com/en. Terminology The following terms are clarified in this document: Stable state (SS): In this document, this term refers exclusively to, and is synonymous with, the protein’s inactive state (IAS). Note that other states may also be stabilized into meta-stable states by certain means. Unstable state (US): This term encompasses all states other than SS, even if they appear computationally meta-stable on the free energy surface. Activated state (AS): The meta-stable working state of the protein. Transition state (TS): The state with the highest free energy along the least-energy pathway on the free energy surface that connects the inactive state to the activated state of the target protein. Intermediate state (IS): The state(s) located at a local minimum along the least-energy pathway, excluding SS and AS.

Xiaohong Zhu, Xiangyu Li, Yaning Hou et al. · 0 citations
Aug 2026

An Explicit Interaction-Prompted Diffusion Framework for High-Fidelity 3D Molecular Generation.

Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.

Huabin Du, Mingyang Wang, M. Luo et al. · 0 citations