Structural Biology

Protein structure prediction, molecular modeling and simulation, and what new structures reveal about biological function.

AI | Bioinformatics | News | Paper Summary | Protein Design | Structural Biology | Tools | Trending
Protpardelle: Revolutionizing Protein Design with All-Atom Generative Model
ByNeermita Bhattacharya July 26, 2024October 22, 2026
In computational biology, the ability to predict and design protein structures with atomic precision has been a long-standing goal. Proteins, as the fundamental components of life, perform many functions driven by their complex three-dimensional structures. Interactions are typically mediated through their side chains, but building an all-atom generative model requires an effective method to handle…
Read More Protpardelle: Revolutionizing Protein Design with All-Atom Generative Model
AI | Bioinformatics | Drug Discovery | LLM | Machine Learning | News | Paper Summary | Protein Design | Structural Biology | Trending
Language Models and Chemistry: Accelerating the Path to Novel Therapeutics
ByNeermita Bhattacharya July 23, 2024October 22, 2026
In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into various scientific domains has significantly advanced, and chemistry is no exception. LLMs can accurately predict properties, design new molecules, optimize synthesis pathways, and accelerate drug and material discovery. Turning LLMs into agents using chemistry-specific tools such as synthesis planners and databases…
Read More Language Models and Chemistry: Accelerating the Path to Novel Therapeutics
AI | Bioinformatics | Drug Discovery | News | Paper Summary | PLM | PPI | Structural Biology | Tools | Trending
ProBASS: A Game-changer in Predicting Mutation Impact on Protein Interactions
ByAnchal Negi July 22, 2024October 22, 2026
Researchers from the Department of Biological Chemistry at Hebrew University, Jerusalem, and the Department of Pharmaceutical Chemistry at the University of California, San Francisco, have developed a landmark tool to forecast how mutations influence protein interactions. This study by Dr. Julia M Shifman, Dr. William DeGrado, and the team describes ProBASS, a language model that…
Read More ProBASS: A Game-changer in Predicting Mutation Impact on Protein Interactions
AI | Bioinformatics | LLM | News | Paper Summary | Protein Design | Structural Biology | Trending
Unveiling HelixProtX: Bridging the Gap in Protein Research with Any-to-Any Generation Capabilities
ByDeotima Chakraborty July 21, 2024October 22, 2026
In an effort to provide a comprehensive solution for protein research, researchers present HelixProtX, a system built around the large multimodal model that facilitates the construction of any-to-any protein modality. It enables the conversion of any input protein modality into any desired protein modality, in contrast to current approaches. The experimental results confirm HelixProtX’s superior…
Read More Unveiling HelixProtX: Bridging the Gap in Protein Research with Any-to-Any Generation Capabilities
Bioinformatics | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending
Streamlining Drug Discovery with Automated Binding Free Energy Calculations
ByNeermita Bhattacharya July 20, 2024October 22, 2026
In the world of drug discovery, predicting how well a drug molecule will bind to its target is like figuring out the best path on a treasure map when you’re lost. Scientists use complex calculations to predict this binding affinity. One powerful method is called “Absolute Binding Free Energy” (ABFE) calculations. These calculations help scientists…
Read More Streamlining Drug Discovery with Automated Binding Free Energy Calculations
AI | Bioinformatics | Biomedical Engineering | Machine Learning | News | Paper Summary | Protein Design | Structural Biology | Trending
Harnessing Computational Tools: How Machine Learning and Molecular Simulations are Transforming Enzyme Design
ByNeermita Bhattacharya July 17, 2024October 22, 2026
Global environmental concerns and the need for sustainable development require new technologies for producing fine chemicals and managing waste efficiently. Nowadays, using enzymes in biocatalysis has proved to be more sustainable. Hence, identifying proteins with excellent biocatalytic properties from the vast array of possible sequences is a task that needs to be simplified. Jiahui Zhou…
Read More Harnessing Computational Tools: How Machine Learning and Molecular Simulations are Transforming Enzyme Design
AI | Bioinformatics | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending
Evaluating 3D Molecular Generative Models: The Impact of GenBench3D on Ligand Conformation Quality
ByNeermita Bhattacharya July 15, 2024October 22, 2026
Evaluating specific properties of molecules from simple SMILE strings isn’t enough. Instead of simple chalkboard formulae, our aim should be to conjure up three-dimensional molecular structures that could fit snugly into tiny pockets within proteins. 3D deep molecular generative models stand out by enabling the generation of molecules based on 3D-dependent properties, such as binding…
Read More Evaluating 3D Molecular Generative Models: The Impact of GenBench3D on Ligand Conformation Quality
AI | Bioinformatics | Computational Models | Drug Discovery | News | Paper Summary | RNA | Structural Biology | Tools | Trending
RNA-FrameFlow: Pioneering 3D RNA Backbone Design with Flow Matching
ByAnshika Gupta July 15, 2024October 22, 2026
A part of systems biology is ‘designing RNA structures,’ which entails planning and organizing molecules of RNA into custom 3D formations with particular uses. RNA plays a dual role in cells, encoding genetic information and performing structural functions. Proteins are somewhat easier to study and model because of the precision nature of this macromolecule, while…
Read More RNA-FrameFlow: Pioneering 3D RNA Backbone Design with Flow Matching
Bioinformatics | Drug Discovery | News | PPI | Structural Biology
The Protein Interaction Explorer (PIE) Platform: Revolutionizing the Study of Protein-Protein Interactions and Ligand Binding Pockets
ByDeotima Chakraborty July 9, 2024October 22, 2026
A web-based tool called Protein Interaction Explorer (PIE) by researchers from Université de Paris, France, was created to support structure-based drug discovery efforts that center on protein-protein interactions (PPIs). PIE is integrated into the database iPPI-DB. Researchers can detect and characterize important variables such as binding pockets, functional binding sites, hot spots, and protein-embedded pockets for…
Read More The Protein Interaction Explorer (PIE) Platform: Revolutionizing the Study of Protein-Protein Interactions and Ligand Binding Pockets
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
The Future of Drug Design: How Machine Learning is Revolutionizing PROTAC Development
ByAnchal Negi July 8, 2024October 22, 2026
As the fight against disease has changed, so have therapeutic strategies. One such new approach is Targeted Protein Degradation (TPD), which attempts to exploit the cell’s natural degradation machinery to get rid of unnecessary proteins. The cornerstone of TPD is PROTACs (Proteolysis-Targeting Chimeras); these molecules are very complex and act like Trojan horses that bring…
Read More The Future of Drug Design: How Machine Learning is Revolutionizing PROTAC Development
Bioinformatics | News | Paper Summary | RNA | Structural Biology | Trending
Decoding RNA-Protein Interactions: Harnessing Computational Tools for Advanced Predictions without High-Throughput Data
ByNeermita Bhattacharya July 8, 2024October 22, 2026
The Central Dogma of Biology describes the process of DNA (Deoxyribonucleic acid) undergoing transcription to form RNA (Ribonucleic acid) and transformation to form proteins finally. However, the interactions between proteins and RNA are also crucial in the optimal operation of various processes in organisms. Improving computational prediction of RNA-protein interactions (RPIs) is important for advancing…
Read More Decoding RNA-Protein Interactions: Harnessing Computational Tools for Advanced Predictions without High-Throughput Data
AI | Bioinformatics | News | Paper Summary | Structural Biology | Trending
Supercomputing Simplified with AI: The APACE Approach to Accelerated Discovery in Biophysics
ByDeotima Chakraborty July 3, 2024October 22, 2026
Researchers from the University of Illinois Urbana-Champaign introduce APACE, AlphaFold2, and advanced computing as a service, a computational framework that manages this TB-sized AI model and database efficiently and optimizes AlphaFold2 to run at scale in high-performance computing systems. Researchers show how APACE is used in the Delta and Polaris supercomputers to speed up the…
Read More Supercomputing Simplified with AI: The APACE Approach to Accelerated Discovery in Biophysics
AI | Bioinformatics | Drug Discovery | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending
Accelerating the Path to New Medicines: Monash’s Innovative AI Tool for Fast and Cost-Effective Drug Discovery
ByAnshika Gupta July 1, 2024October 22, 2026
Improved identification of interactions between proteins and ligands has been invaluable in the progress of computational biology, potentially enhancing drug discovery and revealing more about the molecular world. Traditional approaches tend to utilize structural information, which is sometimes scarce or nonexistent. Enter PSICHIC (PhySIcoCHemICal): a graph convolutional network model, physicochemical and chemotherapeutic interactions in sequence…
Read More Accelerating the Path to New Medicines: Monash’s Innovative AI Tool for Fast and Cost-Effective Drug Discovery
AI | Bioinformatics | Drug Discovery | LLM | News | Paper Summary | Structural Biology | Tools | Trending
MolecularGPT: Revolutionizing Molecular Property Prediction with Open LLMs
ByDeotima Chakraborty June 28, 2024October 22, 2026
Drug discovery requires the fundamental process of molecular property prediction or MPP. However, the shortcomings of previous approaches—crucial for real-world applications—are their limited capacity to generalize for novel and unforeseen jobs and their need for a high number of labeled molecules. The researchers present MolecularGPT for a few-shot MPP to address these issues. Researchers refine…
Read More MolecularGPT: Revolutionizing Molecular Property Prediction with Open LLMs
AI | Algorithm | Bioinformatics | Drug Discovery | News | Paper Summary | RNA | Structural Biology | Tools | Trending
RNAfold: Leveraging Variational Autoencoders for Accurate RNA Tertiary Structure Prediction
ByAnshika Gupta June 26, 2024October 22, 2026
In drug development, the accurate prediction of RNA structures plays a pivotal role. Leveraging the power of machine learning, particularly Variational Autoencoders (VAEs), has emerged as a game-changer in this domain. Recent advancements by Quantori LLC, Cambridge, USA researchers have showcased the superiority of VAE models in RNA folding, surpassing traditional methods with remarkable precision….
Read More RNAfold: Leveraging Variational Autoencoders for Accurate RNA Tertiary Structure Prediction
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FoldToken2: Decoding Proteins with a New Compact Language
ByAnchal Negi June 26, 2024October 22, 2026
Scientists at Westlake University have developed FoldToken2, a novel approach to protein structure representation that is capable of surpassing the coordinates. This innovative method transforms complex 3D protein structures into a series of tokens, like words used in a sentence, to represent key characteristics of protein structure. FoldToken2 works as the Rosetta Stone for proteins,…
Read More FoldToken2: Decoding Proteins with a New Compact Language
AI | Bioinformatics | Drug Discovery | LLM | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
Unlocking New Dimensions in AI-Driven Drug Discovery: TopoFormer Merges Topology and Transformers for Protein-ligand Interaction Predictions
ByDeotima Chakraborty June 25, 2024October 22, 2026
Transformers have shown promise in computational biology, but applying them to biological sequences could cause mispredictions in the downstream processes. A greater range of applications and more accurate downstream predictions are possible with three-dimensional (3D) molecule structures because they are more compatible with Transformer architecture and natural language processing models. This fundamental problem is addressed…
Read More Unlocking New Dimensions in AI-Driven Drug Discovery: TopoFormer Merges Topology and Transformers for Protein-ligand Interaction Predictions
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending
Can Computationally Designed Membrane Protein Analogs Accelerate Drug Discovery?
ByDeotima Chakraborty June 24, 2024October 22, 2026
It is still very difficult to construct complex protein folds from scratch using only computational methods. École Polytechnique Fédérale de Lausanne (EPFL) scientists create intricate folds and soluble counterparts of essential membrane proteins using a powerful deep-learning pipeline. Researchers show that the structural characteristics of unique membrane topologies, including those from G-protein-coupled receptors, may be…
Read More Can Computationally Designed Membrane Protein Analogs Accelerate Drug Discovery?
AI | Bioinformatics | Computational Models | Molecular Dynamics | News | Paper Summary | Structural Biology | Trending
Breaking Barriers in DNA Research: CGeNArate – A Simplified Model for Complex Simulations
ByAnchal Negi June 23, 2024October 22, 2026
Researchers from the Institute for Research in Biomedicine (IRB Barcelona) introduce CGeNArate, a short sequence-dependent coarse-grained model for DNA simulations. CGeNArate solves the challenge of simulating long duplexes of DNA (kilobase-long) accurately without using a lot of computer power in the process. The model accurately reproduces both local and global DNA properties, making it a…
Read More Breaking Barriers in DNA Research: CGeNArate – A Simplified Model for Complex Simulations
AI | Bioinformatics | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending
Streamlining Drug Discovery: How SPARROW Picks the Most Promising Molecules
ByAnshika Gupta June 23, 2024October 22, 2026
In the world of molecular design that is constantly evolving, it is always essential to ensure that along with the structure and properties of newly designed compounds appeal to synthetic chemists; there are ways to synthesize the compound. The ability to have cost-aware decision-making algorithms is a novel opportunity with a focus on the qualified…
Read More Streamlining Drug Discovery: How SPARROW Picks the Most Promising Molecules
AI | Bioinformatics | Drug Discovery | LLM | News | Paper Summary | Structural Biology | Tools | Trending
Binding Site-driven Molecule Design: BindGPT’s Language Modeling and Reinforcement Learning Powered Generative Framework
ByDeotima Chakraborty June 19, 2024October 22, 2026
Understanding the intricate physical interactions between a molecule and its surroundings is necessary to generate unique active molecules for a particular protein, which is a very difficult problem for generative models. In a recent study, scientists from Insilico Medicine and their collaborators introduced a unique generative model called BindGPT that builds 3D molecules inside protein…
Read More Binding Site-driven Molecule Design: BindGPT’s Language Modeling and Reinforcement Learning Powered Generative Framework
AI | Bioinformatics | News | Paper Summary | Protein Design | Structural Biology | Trending
Can Diffusion Models Crack the Protein Structure and Docking Code?
ByAnshika Gupta June 19, 2024October 22, 2026
The field of protein modeling has witnessed remarkable advances in recent years, particularly with the advent of machine learning techniques. A new review by the Massachusetts Institute of Technology, Cambridge researchers covers the basics of diffusion models, molecular representations, generation capabilities, prevailing heuristics, limitations, and forthcoming refinements in computational structural biology. Among these, diffusion models…
Read More Can Diffusion Models Crack the Protein Structure and Docking Code?
AI | Bioinformatics | Drug Discovery | News | Paper Summary | PPI | Structural Biology | Tools | Trending
Proteome-wide PPI Screening: AlphaFastPPi Paves the Way for Large-scale Studies
ByDeotima Chakraborty June 13, 2024October 22, 2026
Despite the challenge of scaling for large-scale screenings, advances in computational structural biology, like AlphaFold-Multimer, have greatly improved the understanding of protein-protein interactions across entire proteomes, enabling large-scale screenings and elucidating system properties within or between organisms. Here, researchers provide AlphaFastPPi, a pipeline based on AlphaPulldown and AlphaFold-Multimer. AlphaFastPPi is a simplified pipeline that uses…
Read More Proteome-wide PPI Screening: AlphaFastPPi Paves the Way for Large-scale Studies
AI | Bioinformatics | Database | Drug Discovery | News | Paper Summary | Structural Biology | Trending
BioNeMo Folds the Human Proteome: A Fused Dataset of 3D Protein Models for Machine Learning Applications
ByAnchal Negi June 13, 2024October 22, 2026
In a recent scientific breakthrough, a team of researchers from NVIDIA and Innophore, USA, came up with a holistic dataset of the predicted protein folding for each member of the proteome of human beings (the complete set of molecules encoded by human genes), representing a great advancement in protein science. Introduction Life has protein as…
Read More BioNeMo Folds the Human Proteome: A Fused Dataset of 3D Protein Models for Machine Learning Applications
AI | Bioinformatics | Deep Learning | LLM | News | Paper Summary | Protein Design | Structural Biology | Tools | Trending
Advancing Pharmaceutical and Industrial Protein Engineering with StructureGPT’s Innovative Structure-to-Sequence Translation Capabilities
ByDeotima Chakraborty June 10, 2024October 22, 2026
Sequence-to-structure predictions are the main focus of current computational methods for reverse translation, which is the process of translating complicated protein structures into sequences. This is how protein design is traditionally understood. The researchers from the InsAIght Research Center and the Public University of Navarra present StructureGPT, a cutting-edge deep learning model that translates complicated…
Read More Advancing Pharmaceutical and Industrial Protein Engineering with StructureGPT’s Innovative Structure-to-Sequence Translation Capabilities
AI | Bioinformatics | Drug Discovery | Health | Medical Informatics | News | Structural Biology | Trending
The AI Behind the Breakthroughs: Meta Llama 3 NIM and the Future of Healthcare and Life Sciences
ByAnchal Negi June 10, 2024October 22, 2026
The healthcare industry is about to change significantly due to the advent of generative AI. Meta Llama 3 NIM, a mighty NVIDIA-developed AI microservice, is at the helm of this revolution. This blog post discusses how Meta Llama 3 NIM has started being adopted across various healthcare sectors, involving some pioneering organizations whose competitiveness and…
Read More The AI Behind the Breakthroughs: Meta Llama 3 NIM and the Future of Healthcare and Life Sciences
AI | Bioinformatics | Drug Discovery | News | Paper Summary | PPI | Proteomics | Structural Biology | Tools | Trending
Leveraging AlphaFold, Rosetta, and Replica-Exchange for Reliable Protein-Protein Docking with AlphaRED
ByDeotima Chakraborty June 8, 2024October 22, 2026
Significant progress has recently been made in predicting protein sequence to structure; however, modeling protein interfaces and complex structures is still tricky, particularly when binding partners undergo large conformational changes. According to earlier research, in only 43% of cases, AF-multimer (AFm) can accurately predict protein complexes. In this work, Johns Hopkins University researchers combine a…
Read More Leveraging AlphaFold, Rosetta, and Replica-Exchange for Reliable Protein-Protein Docking with AlphaRED
AI | Bioinformatics | Machine Learning | Molecular Dynamics | News | Paper Summary | Structural Biology
Protein Shapes in Motion: How Diffold Revolutionizes Conformation Sampling
ByAnchal Negi June 8, 2024October 22, 2026
Our cells are supported critically by proteins, which are also highly affected by their 3D structure. The triumph in computational biology is the ability to predict these structures from a chain of amino acids belonging to protein. Nevertheless, a protein is not static as it can take different shapes, like a dancing girl. Understanding this…
Read More Protein Shapes in Motion: How Diffold Revolutionizes Conformation Sampling
AI | Bioinformatics | Molecular Dynamics | News | Structural Biology | Tools
Predicting Multiple Conformations and Ensembles with AFsample2: An AlphaFold2 Extension
ByAnshika Gupta June 6, 2024October 22, 2026
Acting as enzymes, structural elements, and molecular motors, proteins represent the molecular drive of biological systems, the elucidation of which relies on the explication of conformational transitions. In a recent study by Linköping University, Sweden, researchers got into the issue of protein conformational sampling, stating that there are limitations to current methods and that AFsample2…
Read More Predicting Multiple Conformations and Ensembles with AFsample2: An AlphaFold2 Extension
AI | Bioinformatics | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending
Breaking New Ground in RNA Structure and Sequence Design with RNAFlow
ByAnchal Negi June 3, 2024October 22, 2026
Unsung heroes of our cells are RNAs. These molecules are responsible for many biological processes, from carrying hereditary material to controlling protein production. However, imagine being able to change the functions of RNAs for medical purposes or scientific studies. In their latest research, scientists from the Massachusetts Institute of Technology and Broad Institute of MIT…
Read More Breaking New Ground in RNA Structure and Sequence Design with RNAFlow
AI | Bioinformatics | Machine Learning | News | Paper Summary | Protein Design | Structural Biology | Trending
Scaling Protein Design and Scaffolding to the Structural Universe with Genie 2’s Holistic Approach
ByDeotima Chakraborty June 1, 2024October 22, 2026
Genie is a promising method for designing proteins that use expressive SE(3)-equivariant attention and simple Gaussian noise to represent structures asymmetrically during forward and backward processes. Researchers from Columbia University and Rutgers University introduced Genie 2, which is extended through architectural improvements and enormous data augmentation to capture a broader and more diversified protein structure…
Read More Scaling Protein Design and Scaffolding to the Structural Universe with Genie 2’s Holistic Approach
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Predicting Ligand-specific Protein-ligand Complex Structure with DynamicBind: A Novel Deep Equivariant Generative Model
ByAnshika Gupta May 31, 2024October 22, 2026
In the ever-changing environment of computational biology, predicting the structure of protein-ligand complexes remains a vital task with far-reaching ramifications for drug development and molecular biology. DynamicBind, a pioneering model described in a Nature Communications paper, is revolutionizing the field of protein-ligand interaction investigations. This novel technique, pioneered by Wei Lu, Jixian Zhang, Shuangjia Zheng,…
Read More Predicting Ligand-specific Protein-ligand Complex Structure with DynamicBind: A Novel Deep Equivariant Generative Model
AI | Bioinformatics | Drug Discovery | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending
Accelerating Drug Discovery with QComp’s Innovative QSAR-based Data Completion Framework
ByAnshika Gupta May 29, 2024October 22, 2026
In drug discovery, the accurate prediction of chemical activities is paramount. The conventional forms of QSAR that were initially developed and widely used are the traditional or classical approach of QSAR modeling. However, there is a fundamentally new approach that redefines the future of data completion in drug discovery, called QSAR-Complete (QComp), developed by the…
Read More Accelerating Drug Discovery with QComp’s Innovative QSAR-based Data Completion Framework
AI | Bioinformatics | Deep Learning | Drug Discovery | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending
The False Positive Problem in Drug Discovery: Can ChemFH Be the Solution?
ByCBIRT May 28, 2024October 22, 2026
In the drug discovery process, high-throughput screening quickly evaluates a wide range of chemical compounds to find hit compounds for certain biological targets. False-positive results, on the other hand, cause delays in hit compound screening and waste money. In order to address this, scientists from Central South University, China, and collaborators have developed ChemFH, an…
Read More The False Positive Problem in Drug Discovery: Can ChemFH Be the Solution?
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending
Can Deep Learning Methods Tackle Practical Protein-Ligand Docking Challenges? Introducing PoseBench
ByAnchal Negi May 27, 2024October 22, 2026
Scientists from the University of Missouri, Columbia introduced PoseBench, the first exhaustive benchmark for practical protein-ligand docking, enabling rigorous evaluation of deep learning docking methods on predicted protein structures, multiple concurrently binding ligands, and targets without known binding pockets. It introduces multi-ligand benchmark datasets and reveals limitations of current deep learning approaches in generalizing to…
Read More Can Deep Learning Methods Tackle Practical Protein-Ligand Docking Challenges? Introducing PoseBench
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ProteinDPO: A Novel Technique for Aligning Generative Protein Models with Experimental Fitness
ByAnshika Gupta May 27, 2024October 22, 2026
Lives would not be made perfect without proteins since they are vital macromolecules that provide stability in their function. The study from Stanford University and Arc Institute presents a new concept known as ProteinDPO, a method that synchronizes generative models and performance landscapes with experiments in a bid to enhance stability predictions and protein design…
Read More ProteinDPO: A Novel Technique for Aligning Generative Protein Models with Experimental Fitness
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StarFunc: Revolutionizing Protein Function Prediction Leveraging Template-based and Deep Learning Approaches
ByAnchal Negi May 22, 2024October 22, 2026
A recent study by the Chengxin Zhang lab from the University of Michigan introduced StarFunc, an accurate protein function prediction approach integrating cutting-edge deep learning with template data from sequence homology, protein interactions, structural similarities, and domain families. Rather than picking sides, however, StarFunc combines two worlds to create a unified framework. This creative idea…
Read More StarFunc: Revolutionizing Protein Function Prediction Leveraging Template-based and Deep Learning Approaches
AI | Bioinformatics | LLM | Machine Learning | News | Paper Summary | Protein Design | Structural Biology | Tools | Trending
Revolutionizing Antibody Structure-based Design with AntiFold: A Breakthrough in Inverse Folding Technology
ByAnshika Gupta May 18, 2024October 22, 2026
Scientists from the University of Oxford along with the Technical University of Denmark have introduced AntiFold: a specialized model for antibody design that will help in improving sequence recovery in the most important regions and, at the same time, will aid in the zero-shot affinity prediction. It is the antibody structure that is the input,…
Read More Revolutionizing Antibody Structure-based Design with AntiFold: A Breakthrough in Inverse Folding Technology
Bioinformatics | News | Paper Summary | Structural Biology | Tools | Trending
Unraveling the Mysteries of Molecular Dynamics: MDverse Sheds Light on Dark Matter
ByDeotima Chakraborty May 13, 2024October 22, 2026
Molecular dynamics (MD) files have accumulated in generalist data repositories due to the rise of open science and the lack of a worldwide repository specifically for MD simulations. This data is known as the “dark matter” of MD, which is technically accessible but not indexed, curated, or readily searchable. Researchers discovered and indexed over 250,000…
Read More Unraveling the Mysteries of Molecular Dynamics: MDverse Sheds Light on Dark Matter
AI | Bioinformatics | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending | Web Server
The Ultimate Molecular Explorer: AlphaFold 3, DeepMind’s AI Marvel to Unravel the Mysteries of All Life’s Molecules
ByAnchal Negi May 10, 2024October 22, 2026
DeepMind and Isomorphic Labs unveil AlphaFold 3, an AI model that predicts structures and interactions of all biomolecules like proteins, nucleic acids, ligands, and more. A protein structure predicting system, AlphaFold 2, launched in 2021, had an accuracy that was unprecedented before. With an updated diffusion-based architecture, AlphaFold 3 demonstrates significantly improved accuracy over specialized…
Read More The Ultimate Molecular Explorer: AlphaFold 3, DeepMind’s AI Marvel to Unravel the Mysteries of All Life’s Molecules
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
Designing Multi-Tasking Drugs: How POLYGON Makes Polypharmacology Easier with Deep Generative Chemistry
ByDeotima Chakraborty May 9, 2024October 22, 2026
Drugs with polypharmacology, or substances that block several proteins, offer a wide range of uses but are challenging to create. To overcome this difficulty, researchers from the University of California San Diego created POLYGON, a generative reinforcement learning-based method for polypharmacology. In order to develop novel molecular structures, POLYGON embeds chemical space and repeatedly samples…
Read More Designing Multi-Tasking Drugs: How POLYGON Makes Polypharmacology Easier with Deep Generative Chemistry
AI | Bioinformatics | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending
REvoLd: Accelerating Drug Discovery Through Evolutionary Algorithm-Driven Ultra-Large Library Screening in Rosetta
ByAnshika Gupta May 5, 2024October 22, 2026
An endeavor to create new medicinal compounds that can reach great heights, though challenging, remains vital. The advent of computer tools has led to extra creative routes to define and improve molecules within this chemical combinatorial area. Among these achievements is REvoLd, a system developed by researchers from Leipzig University, in collaboration with Vanderbilt University,…
Read More REvoLd: Accelerating Drug Discovery Through Evolutionary Algorithm-Driven Ultra-Large Library Screening in Rosetta
AI | Bioinformatics | LLM | News | Paper Summary | PPI | Tools | Trending
Revolutionizing Protein-Protein Interaction Prediction with ProLLM’s Chain-of-Thought Enhanced LLM
ByDeotima Chakraborty May 2, 2024October 22, 2026
Predicting protein-protein interactions (PPIs) is essential to comprehend biological processes and illnesses. Conventional machine learning techniques ignore nonphysical linkages made through intermediary proteins in favor of direct physical interactions. By converting structured data into natural language prompts, mapping protein linkages into sentences, finding indirect connections, and following the journey from upstream to downstream, Large Language…
Read More Revolutionizing Protein-Protein Interaction Prediction with ProLLM’s Chain-of-Thought Enhanced LLM
Bioinformatics | News | Paper Summary | Protein Design | Structural Biology | Trending | Web Server
Protein Design Made Easy with Damietta Server: A Comprehensive Toolkit
ByDeotima Chakraborty May 1, 2024October 22, 2026
The increasing relevance of protein design across a range of scientific fields drives the creation of integrative computational platforms that improve tool accessibility and interoperability. In order to do this, scientists present a web-based toolbox that expands upon the Damietta protein design engine by utilizing a framework for tensorized energy calculations. In addition to other…
Read More Protein Design Made Easy with Damietta Server: A Comprehensive Toolkit
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
ETH Zurich Chemists Unveil DRAGONFLY: New AI Approach for Rapid Drug Design from Protein Structures
ByAnchal Negi April 30, 2024October 22, 2026
Centuries of scientific invention have been driven by the search for new drugs to fight against diseases. Since medicinal plants became valuable sources of medicine from synthetic chemistry, we have consistently improved our capacity to design molecules with therapeutic potential. However, the traditional drug discovery process is time-consuming, costly, and fraught with challenges. It is…
Read More ETH Zurich Chemists Unveil DRAGONFLY: New AI Approach for Rapid Drug Design from Protein Structures
AI | Bioinformatics | Machine Learning | News | Paper Summary | RNA-Seq | Structural Biology | Tools | Trending
Cracking the RNA Inverse Folding Puzzle with RiboDiffusion’s Generative Diffusion Approach
ByAnshika Gupta April 28, 2024October 22, 2026
Molecular biology scientists have been captivated by the complex way RNA folds. A recent study by Beihang and Nanjing University researchers in China introduces the RiboDiffusion model. This new method looks at predicting protein structure at the atomic level and is more advanced than existing methods like StructGNN and GVP-GNN. RiboDiffusion: A Game Changer Designing…
Read More Cracking the RNA Inverse Folding Puzzle with RiboDiffusion’s Generative Diffusion Approach
AI | Bioinformatics | Machine Learning | Molecular Dynamics | News | Paper Summary | Tools | Trending
GEMS: DeepMind’s Novel Approach to Accurate Biomolecular Dynamics through Machine-Learned Quantum-Mechanical Force Fields
ByAnchal Negi April 24, 2024October 22, 2026
Think about peering into the intricate world of biomolecules, those complex assemblies that direct the symphony of life. Scientists have long relied on molecular dynamics (MD) simulations to watch them dance, but these often require costly quantum-mechanical calculations to get accurate results. However, a groundbreaking new technique called GEMS (general approach for constructing accurate machine-learned…
Read More GEMS: DeepMind’s Novel Approach to Accurate Biomolecular Dynamics through Machine-Learned Quantum-Mechanical Force Fields
AI | Bioinformatics | Computational Models | LLM | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending
Decoding Proteins with DeProt: A Breakthrough in Language Modeling through Quantized Structure and Disentangled Attention
ByDeotima Chakraborty April 22, 2024October 22, 2026
Conventional representational models of protein functions have demonstrated notable performance in downstream tasks; nevertheless, they frequently do not incorporate explicit information on protein structure, which is essential for comprehending the connection between protein sequences and functions. To fill this void, scientists provide DeProt, a Transformer-based protein language model that can take into account the shapes…
Read More Decoding Proteins with DeProt: A Breakthrough in Language Modeling through Quantized Structure and Disentangled Attention
AI | Bioinformatics | Database | Machine Learning | News | Paper Summary | PPI | Structural Biology | Trending
A New Era in Interactomics: Exploring Predictomes, the AlphaFold-Modeled Protein Interaction Database
ByAnchal Negi April 20, 2024October 22, 2026
Imagine a teeming city where proteins are the movers and shakers. Each protein intertwines with others in partnerships that are vital to every cellular process. Nevertheless, unraveling these interactions, which are called protein-protein interactions (PPIs), forms the basis for both biology and drug development. Detecting PPIs has been akin to overhearing hushed conversations in a…
Read More A New Era in Interactomics: Exploring Predictomes, the AlphaFold-Modeled Protein Interaction Database
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Revolutionizing Molecular Linker Design with DiffLinker: The Power of Equivariant 3D-conditional Diffusion Models
ByDeotima Chakraborty April 19, 2024October 22, 2026
The effective paradigm of molecular linkers in drug discovery is crucial for obtaining relevant candidate molecules in early-stage drug development. In this study, researchers present DiffLinker, a three-dimensional conditional diffusion model for molecular linker design that is E(3)-equivariant. A synthetically available model called DiffLinker generates molecules from disconnected fragments that include all of the original…
Read More Revolutionizing Molecular Linker Design with DiffLinker: The Power of Equivariant 3D-conditional Diffusion Models

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Structural Biology

The Protein Interaction Explorer (PIE) Platform: Revolutionizing the Study of Protein-Protein Interactions and Ligand Binding Pockets

Protein Shapes in Motion: How Diffold Revolutionizes Conformation Sampling

Predicting Multiple Conformations and Ensembles with AFsample2: An AlphaFold2 Extension

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