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代表性文献（按质量分排序，前 60 条）

• Insights into Lithium Diffusion in Crystalline and Amorphous Solid Electrolytes with Machine Learning Interatomic Potentials
  2026 · AI 驱动科学发现 · Amorphization is a widely used approach to tune the ionic conductivity in solid electrolytes, but its effect in different anion chemistries remains poorly understood. In this work, we employ molecular dynamics (MD) simul…
• Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential
  2025 · AI 驱动科学发现 · Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge. Here, we leverage a fine-tuned machine…
• Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction
  2025 · AI 驱动科学发现 · Molecular crystal structure prediction (CSP) faces a persistent computational bottleneck: it requires exhaustive sampling of vast packing landscapes while resolving energy differences of only a few kJ·mol-1. We introduce…
• Cerebra: A Computationally Efficient Framework for Accurate Protein Structure Prediction
  2024 · AI 驱动科学发现 · Abstract Remarkable progress has been made in the field of protein structure prediction in the past years. State-of-the-art methods like AlphaFold2 and RoseTTAFold2 achieve prediction accuracy close to experimental struc…
• The end of protein structure prediction: Improving prediction accuracy in chimeric proteins by windowed multiple sequence alignment
  2024 · AI 驱动科学发现 · Abstract AlphaFold2 has predicted the structures of almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins. Here we demonstrate th…
• The importance of sampling the dynamical modes: Reevaluating benchmarks for invariant and equivariant features of machine learning potentials for simulation of free energy landscapes.
  2024 · AI 驱动科学发现 · Machine learning interatomic potentials (MLIPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computati…
• Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile.
  2024 · AI 驱动科学发现 · Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions am…
• Cerebra: a computationally efficient framework for accurate protein structure prediction
  2024 · AI 驱动科学发现 · A bstract Remarkable progress has been made in the field of protein structure prediction in the past years. State-of-the-art methods like AlphaFold2 and RoseTTAFold2 achieve prediction accuracy close to experimental stru…
• Conditioned Protein Structure Prediction
  2023 · AI 驱动科学发现 · Abstract Deep learning based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integrati…
• Machine Learning Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks
  2024 · AI 驱动科学发现 · The detailed understanding of the microscopic structure of amorphous phases of metal-organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experiment…
• Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks
  2024 · AI 驱动科学发现 · Protein secondary structure prediction is useful for many applications. It can be considered a language translation problem, that is, translating a sequence of 20 different amino acids into a sequence of secondary struct…
• Basic Local Alignment Search Tool (Blast) and Multiple Sequence Alignment
  2023 · AI 安全与对齐 · This chapter look at the topic of pairwise sequence comparison by describing the Basic Local Alignment Search Tool (BLAST). It discusses multiple sequence alignment programs with an emphasis on the first popular program …
• Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule
  2023 · AI 驱动科学发现 · We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species ex…
• Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
  2023 · AI 驱动科学发现 · Abstract Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. …
• Protein Structure Prediction
  2023 · AI 驱动科学发现 · This chapter concentrates on protein structure prediction programs. It provides strong foundational knowledge of protein structures and the Protein Data Bank. The chapter reviews how secondary and tertiary structures of …
• Amino Acids as Multifunctional Molecules in Plants: From Fundamental Metabolism to Precision Agriculture.
  2026 · 智慧农业 · Amino acids are organic compounds that serve as the fundamental building blocks of proteins and are additionally responsible for a multitude of other biological functions. This review synthesizes recent evidence elucidat…
• Understanding the solvent effect on ion-pair dissociation at the air-water interface by machine learning interatomic potential
  2026 · AI 驱动科学发现 · Abstract Solvent interactions play a key role in ion-pair formation and dissociation at the air–water interface, but capturing these effects requires ab initio accuracy and explicit solvent coordinate, which are often be…
• Protein Structure Prediction
  2026 · AI 驱动科学发现 · This chapter presents experimental and computational approaches to determining protein structures, focusing on secondary and tertiary levels. It describes how researchers use X-ray crystallography, NMR spectroscopy, and …
• Fundamentals of Generative AI
  2026 · AI 驱动科学发现 · Imagine having the power to create entirely new pieces of music, generate lifelike images, or craft realistic synthetic data—all from scratch. That’s the magic of generative models. These models go beyond simply predicti…
• Predicting Crystal Structures and Ionic Conductivities in Li ₃ YCl _6−x Br _x Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential
  2026 · AI 驱动科学发现 · Understanding ionic transport in halide solid electrolytes (SEs) is essential for advancing next‐generation solid‐state batteries. This work demonstrates the effectiveness of fine‐tuning the Crystal Hamiltonian Graph Net…
• SMILES alignment: a dynamic programming approach for the alignment of metabolites and other small organic molecules
  2025 · AI 安全与对齐 · Abstract Background There is a need for computational approaches to compare small organic molecules based on chemical similarity or for evaluating biochemical transformations. No tool currently exists to generate global …
• Improving protein structure prediction with multicom
  2025 · AI 驱动科学发现 · Proteins play essential roles in biological processes and understanding their three-dimensional (3D) structures is vital for revealing their complex functions. This study addresses the challenge of accurately predicting …
• Workflow for Protein Structure Prediction, Refinement, and Validation v1
  2025 · AI 驱动科学发现 · This protocol provides a reproducible computational workflow for protein structure prediction using a combination of freely available software, open-source pipelines, and specialized protein structure prediction services…
• Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.
  2024 · AI 驱动科学发现 · Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modelin…
• Response Matching for Generating Materials and Molecules.
  2024 · AI 驱动科学发现 · Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacem…
• Single-sequence protein structure prediction using supervised transformer protein language models
  2022 · AI 驱动科学发现 · Abstract It remains challenging for single-sequence protein structure prediction with AlphaFold2 and other deep learning methods. In this work, we introduce trRosettaX-Single, a novel algorithm for singlesequence protein…
• TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks
  2020 · AI 安全与对齐 · Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactio…
• TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks
  2020 · AI 安全与对齐 · Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPIs networks for many mod…
• The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction
  2020 · AI 驱动科学发现 · Prediction of the three-dimensional (3D) structure of a protein from its sequence is important for studying its biological function. With the advancement in deep learning contact distance prediction and residue-residue c…
• Prediction of Weak Alignment from Molecule Structure
  2024 · AI 安全与对齐 · Residual dipolar couplings arise from the preferential orientation of a molecule in the anisotropic environment of an alignment medium. The characteristics and degree of alignment, transmitted from the alignment medium t…
• Efficient and Accurate pKa Prediction Enabled by Pre-Trained Machine-Learned Interatomic Potentials
  2024 · AI 驱动科学发现 · Quickly and accurately predicting the pKa of small molecules is an important unsolved challenge in computational chemistry: while approaches based on electronic structure theory have shown great promise, the utility of t…
• Prediction of transmembrane protein topology
  2023 · AI 驱动科学发现 · Abstract Membrane proteins fulfil many critical cellular functions, and make up a large fraction of all proteins. In fact, it has recently been estimated that as many as 35–-40% of all yeast genes may code for integral m…
• Protein-protein interaction prediction via structure-based deep learning
  2023 · AI 驱动科学发现 · Abstract Protein-protein interactions (PPIs) play an essential role in life activities. Many machine learning algorithms based on protein sequence information have been developed to predict PPIs. However, these models ha…
• Structure prediction of alternative protein conformations
  2023 · AI 驱动科学发现 · Abstract Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins in the conformatio…
• Protein Structure Prediction
  2023 · AI 驱动科学发现 · The primary resource for protein structure is PDB. This database stores the coordinates of protein structures that have been solved either using X-rays or NMR. There exists a huge literature on techniques for predicting …
• The prediction challenge
  2023 · AI 驱动科学发现 · Abstract Genome projects will soon be producing hundreds of kilobases of raw sequence a day. A major effort in biocomputing will be the analysis of sequence data in terms of functional and structural characterization. Ho…
• Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction
  2019 · AI 驱动科学发现 · Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have r…
• The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool
  2016 · AI 驱动科学发现 · The GOR method of protein secondary structure prediction is described. The original method was published by Garnier, Osguthorpe, and Robson in 1978 and was one of the first successful methods to predict protein secondary…
• Machine learning scheme for fast extraction of chemically interpretable interatomic potentials
  2016 · AI 驱动科学发现 · We present a new method for a fast, unbiased and accurate representation of interatomic interactions. It is a combination of an artificial neural network and our new approach for pair potential reconstruction. The potent…
• DOCK/PIERR: Web Server for Structure Prediction of Protein–Protein Complexes
  2014 · AI 驱动科学发现 · In protein docking we aim to find the structure of the complex formed when two proteins interact. Protein-protein interactions are crucial for cell function. Here we discuss the usage of DOCK/PIERR. In DOCK/PIERR, a unif…
• The MULTICOM Protein Tertiary Structure Prediction System
  2014 · AI 驱动科学发现 · With the expansion of genomics and proteomics data aided by the rapid progress of next-generation sequencing technologies, computational prediction of protein three-dimensional structure is an essential part of modern st…
• Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox
  2012 · AI 驱动科学发现 · A protein molecule is a covalent chain of amino acid residues. Although it is topologically linear, in physiological conditions it folds into a unique (though flexible) three-dimensional structure. This structure, which …
• Relating Protein Structure and Function Through a Bijection and Its Implications on Protein Structure Prediction
  2012 · AI 驱动科学发现 · Proteins are studied by measuring different properties, typically the chemical structure and biochemical activity. Given that these measurements are done on the same protein molecule, they must be related. Despite the fa…
• Protein Structure Prediction by Protein Threading
  2010 · AI 驱动科学发现 · The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness…
• Protein Structure Prediction Using Threading
  2008 · AI 驱动科学发现 · This chapter discusses the protocol for computational protein structure prediction by protein threading. First, we present a general procedure and summarize some typical ideas for each step of protein threading. Then, we…
• Scoring Functions for De Novo Protein Structure Prediction Revisited
  2008 · AI 驱动科学发现 · De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational featur…
• Algorithms for Multiple Protein Structure Alignment and Structure-Derived Multiple Sequence Alignment
  2008 · AI 驱动科学发现 · Primary amino acid content and the geometry of the folded protein 3D structure are major parameters of protein function. During the course of evolution the protein 3D structure is more preserved than its primary sequence…
• Investigating the Relationship Between Site-specific Yield and Protein of Cereal Crops
  2005 · 智慧农业 · Agronomists use overlaying protein and yield maps to identify factors limiting cereal crop growth and development. Management decisions can be derived from knowing what and where these limiting factors are. In using prot…
• Scoring Functions for ab initio Protein Structure Prediction
  2003 · AI 驱动科学发现 · The native conformation of a protein is generally assumed to be the one with the lowest free energy ( 1 ). The successful prediction of protein structure depends on the surmounting of three subproblems: (1) choosing a re…
• Polymer-Agent: Large Language Model Agent for Polymer Design.
  2026 · AI Agent 生态 · On-demand polymer discovery is essential across various industries, from biomedical applications to reinforcement materials. Experiments with polymers involve a long trial-and-error process that consumes extensive resour…
• AI-derived Protein Structures Validation: AlphaFold2 Models in the Twilight Zone
  2026 · AI 安全与对齐 · In any field, unquestioningly accepting artificial intelligence (AI) results should be considered bad practise. Here, we devised a comparative modelling-based strategy for validating protein structures that exploits the …
• Multiple protein structure alignment at scale with FoldMason.
  2026 · AI 安全与对齐 · Protein structure is conserved beyond sequence, making multiple structural alignment (MSTA) essential for analyzing distantly related proteins. Computational prediction methods have vastly extended our repository of avai…
• Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications.
  2026 · AI 安全与对齐 · The three-dimensional structure of a protein underpins its biological function, making structure determination and prediction central challenges in structural biology. Although experimental techniques such as X-ray cryst…
• Antibody-enabled structural biology and AI-driven antibody design.
  2026 · AI 安全与对齐 · Membrane proteins govern essential cellular processes, including ion transport, signal transduction, and molecular recognition, and collectively represent more than half of all current therapeutic targets. Yet their stru…
• RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging structure and sequence information.
  2026 · AI 安全与对齐 · Recent advances in de novo protein design have enabled the generation of diverse novel proteins. However, a fundamental challenge remains: even when an amino acid sequence is designed with the target structure as the mos…
• Computational hybrid approach of PBPK simulation and CGCNN models for human intravenous pharmacokinetics.
  2026 · AI 安全与对齐 · In drug discovery, it is not sufficient for a lead compound to exhibit high binding affinity for the target protein alone. It is equally important to predict an optimal pharmacokinetic (PK) profile that reflects the phys…
• Foldify: Web Application for Protein Structure Prediction.
  2026 · AI 驱动科学发现 · Protein structure prediction models released in recent years have presented tectonic changes in the field of structural biology. However, their potential has not yet been harnessed to its fullest due to their demands on …
• The accuracy of electrostatic interactions captured by AI protein structure prediction models.
  2026 · AI 驱动科学发现 · A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements reveal this variant has twice the helical structure of wi…
• Protein Structure Prediction Methods.
  2026 · AI 驱动科学发现 · Protein structure prediction, a fundamental challenge emerging from the protein folding problem, forms the basis of modern computational biology. This field addresses the critical question of how the amino acids sequence…
• Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.
  2026 · AI 驱动科学发现 · Here we present a series of tutorials demonstrating the use of various methods which integrate structural mass spectrometry (MS) data with computational protein structure prediction methods. We give usage examples of wid…

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