Deep Learning

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CleaveNet Enables Scalable and Targeted Protease Substrate Design for Diagnostics and Therapeutics
BySaniya Sayyed January 13, 2026October 22, 2026

Scientists from MIT and Microsoft Research present CleaveNet, an AI-based pipeline that merges predictive and generative modeling for end-to-end peptide (short protein) design. By validating the system on matrix metalloproteinases (MMPs), the team demonstrates that AI can uncover novel cleavage motifs, engineer selective substrates, and overcome ever-existing challenges in protease promiscuity. Balancing Efficiency, Selectivity, and…

Read More CleaveNet Enables Scalable and Targeted Protease Substrate Design for Diagnostics and Therapeutics
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Structural Biology | Tools | Trending

Redefining SBDD: PackDock Speeds Up Accurate Protein-Ligand Docking with Diffusion Models
BySaniya Sayyed January 10, 2026October 22, 2026

Researchers from the University of Shanghai, China, created PackDock, a diffusion-based side-chain packing model that predicts diverse side-chain conformations in protein binding pockets, both with and without ligands. PackDock is compatible with other docking algorithms and achieves near-rigid docking accuracy while being significantly faster than traditional docking methods. The Promise and Limits of Drug Design…

Read More Redefining SBDD: PackDock Speeds Up Accurate Protein-Ligand Docking with Diffusion Models
Bioinformatics | Cell | Deep Learning | Genomics | News | Paper Summary | Trending

Human Genome in 4D: Mapping Structure, Function, and Dynamics
ByAnchal Negi January 10, 2026October 22, 2026

An integrated atlas of the human 4D genome just dropped, and it is breathtaking in its ambition. In a massive consortium effort, researchers from various institutions, including UMass Chan Medical School, Northwestern University, and other collaborators, have assembled one of the most comprehensive views to date of how the human genome folds and functions in…

Read More Human Genome in 4D: Mapping Structure, Function, and Dynamics
AI | Cancer | Deep Learning | Genomics | News | OMICS | Paper Summary | Tools | Trending

DeepSomatic: Redefining Somatic Variant Detection in the Genomic Sequencing Era
ByJainab Shaikh January 2, 2026October 22, 2026

Scientists at UC Santa Cruz, Google, the National Institutes of Health, and partner institutes have developed DeepSomatic, a deep-learning tool that detects cancer-related DNA alterations (somatic variations) from both short-read and long-read sequencing. Unlike other tools, it can operate with whole-genome, exome, tumor-normal, tumor-only, and FFPE samples. The team generated and publicly released five matched…

Read More DeepSomatic: Redefining Somatic Variant Detection in the Genomic Sequencing Era
AI | Bioinformatics | Deep Learning | News | Paper Summary | Proteomics | Trending

How SPURS Unlocks Scalable and Accurate Protein Stability Prediction
ByAnchal Negi December 31, 2025October 22, 2026

Protein engineering is in the middle of a quiet revolution, and a new study from researchers at the School of Computational Science and Engineering, Georgia Institute of Technology, pushes that revolution a big step forward, introducing SPURS (stability prediction using a rewired strategy). Their work, published in Nature Communications, tackles a longstanding challenge in computational…

Read More How SPURS Unlocks Scalable and Accurate Protein Stability Prediction
AI | Bioinformatics | Drug Discovery | Foundation Model | News | Paper Summary | Structural Biology | Trending

Placing Every Atom Right: PEARL’s Deep Learning Approach to Drug Discovery
ByAnchal Negi December 22, 2025October 22, 2026

Genesis Molecular AI and NVIDIA have introduced PEARL (Placing Every Atom in the Right Location), a deep learning foundation model designed for large-scale protein–ligand cofolding in computational drug discovery. By leveraging synthetic training data, SO(3)-equivariant diffusion architectures, and controllable inference, PEARL achieves more accurate and physically valid binding pose predictions, outperforming AlphaFold 3 and other leading…

Read More Placing Every Atom Right: PEARL’s Deep Learning Approach to Drug Discovery
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Trending

D-I-TASSER Outperforms AlphaFold? A New Frontier in Protein Structure Modeling
ByDeotima Chakraborty May 29, 2025October 22, 2026

The requirement and utility of conventional force field-based folding simulations have been called into question by the overwhelming success of deep learning techniques in protein structure prediction. Researchers from the University of Michigan and the National University of Singapore presented a hybrid method called deep-learning-based iterative threading assembly refinement (D-I-TASSER), which combines iterative threading fragment…

Read More D-I-TASSER Outperforms AlphaFold? A New Frontier in Protein Structure Modeling
AI | Bioinformatics | News | Paper Summary | Protein Design | Structural Biology | Trending

Overlapping Genes, Unfolding Insights: Synthetic Biology Meets AI
ByDeotima Chakraborty May 22, 2025October 22, 2026

Given that the sharing of codon nucleotides dramatically reduces the size of protein sequences, viruses in nature often generate overlapping genes (OLG) in alternate reading frames of the same nucleotide sequence. The question of whether amino acid sequences are sufficiently degenerate with regard to protein folding to permit the extensive overlap of arbitrary pairs of…

Read More Overlapping Genes, Unfolding Insights: Synthetic Biology Meets AI
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Structural Biology | Trending

Fast, Accurate, and Flexible: How FLOWR is Transforming Molecular Design
ByAnchal Negi April 23, 2025October 22, 2026

In a recent breakthrough in AI-driven drug discovery, researchers from Pfizer, AstraZeneca, and Chalmers University unveiled a new generative framework called FLOWR, designed to revolutionize structure-based ligand generation. Their collaborative work bridges machine learning, computational chemistry, and pharmaceutical research and is set to address some of the most persistent challenges in early-stage drug design. Why…

Read More Fast, Accurate, and Flexible: How FLOWR is Transforming Molecular Design
AI | Bioinformatics | LLM | News | Paper Summary | Trending

Meet AgentRxiv: A Collaborative Framework for AI Research Agents to Accelerate Discovery
ByDeotima Chakraborty April 13, 2025October 22, 2026

Hundreds of scientists come together through collaborative efforts to accomplish a shared goal during scientific discovery processes. Current workflows generate research independently, yet they cannot continually enhance previous discoveries because researchers work separately from one another. AgentRxiv, introduced by researchers from Johns Hopkins University and ETH Zurich, presents a system that solves these challenges by…

Read More Meet AgentRxiv: A Collaborative Framework for AI Research Agents to Accelerate Discovery
AI | Bioinformatics | Deep Learning | Drug Discovery | Molecular Dynamics | News | Paper Summary | Structural Biology | Trending

MolAI: The Deep Learning Model Transforming Molecular Descriptor Generation
ByAnchal Negi April 12, 2025October 22, 2026

Recent years have witnessed significant advancements in the fields of artificial intelligence (AI) and machine learning (ML), catalyzing a revolution in cheminformatics and drug discovery. These advancements have enabled scientists to predict molecular properties, design novel compounds with desired characteristics, and estimate drug/bio-macromolecular interactions with unprecedented accuracy. At the heart of these breakthroughs lies the…

Read More MolAI: The Deep Learning Model Transforming Molecular Descriptor Generation
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Tools | Trending

Dyna-1: Unraveling Protein Dynamics with Deep Learning and NMR Data
ByDeotima Chakraborty April 5, 2025October 22, 2026

There are more than 100 NMR relaxation datasets in the Biological Magnetic Resonance Data Bank (BMRB), which show an observable motion in µs-ms. NMR signals can be broadened beyond detection by millisecond motions, leaving residues unassigned in chemical shift datasets of about 10,000 proteins. Deep learning models were taught to predict missing assignments caused by…

Read More Dyna-1: Unraveling Protein Dynamics with Deep Learning and NMR Data
AI | Bioinformatics | News | OMICS | Paper Summary | Trending

Lyra: A Revolutionary AI Model for Biological Sequence Modeling
ByAnchal Negi April 5, 2025October 22, 2026

Researchers from the Broad Institute of MIT and Harvard, Harvard University, Carnegie Mellon University, and Howard Hughes Medical Institute, the study introduces Lyra—a groundbreaking deep-learning model designed for efficient biological sequence modeling. The Need for Efficient Biological Sequence Modeling Understanding the intricate relationships between DNA, RNA, and protein sequences is at the heart of modern…

Read More Lyra: A Revolutionary AI Model for Biological Sequence Modeling
AI | Bioinformatics | Deep Learning | News | Paper Summary | Protein Design | RNA | Structural Biology | Tools

RNA-BAnG: A Deep Learning Breakthrough for Designing Protein-Binding RNA Sequences
ByDeotima Chakraborty March 19, 2025October 22, 2026

One of the most significant challenges in computational and experimental biology is creating RNA molecules that interact with particular proteins. The practical use of current computational methods is limited by their requirement for a significant number of empirically confirmed RNA sequences for every individual protein or a thorough understanding of RNA structure. Researchers have developed…

Read More RNA-BAnG: A Deep Learning Breakthrough for Designing Protein-Binding RNA Sequences
AI | Bioinformatics | Cancer | Deep Learning | Machine Learning | News | Paper Summary | Trending

Revolutionizing Cancer Diagnosis and Treatment: The Transformative Role of AI in Oncology
ByDeotima Chakraborty February 3, 2025October 22, 2026

Artificial intelligence is revolutionizing the diagnosis, treatment, and treatment of cancer. AI overcomes the drawbacks of traditional diagnostic methods by increasing accuracy to optimize treatment plans and customize medicines for various malignancies, such as lung, breast, colorectal, liver, stomach, esophageal, cervical, prostate, and skin. In this paper, researchers from Zhengzhou University draw attention to the…

Read More Revolutionizing Cancer Diagnosis and Treatment: The Transformative Role of AI in Oncology
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Tools

NuFold: A Revolutionary Leap in RNA Tertiary Structure Prediction
ByAnchal Negi February 1, 2025October 22, 2026

RNA, often overshadowed by its protein counterpart, plays an essential role in the molecular machinery of life. Predicting RNA’s three-dimensional (3D) structure is a vital challenge in understanding its function, but it’s a task riddled with complexities. Enter NuFold, an innovative RNA structure prediction method that promises to advance our understanding of RNA folding mechanisms….

Read More NuFold: A Revolutionary Leap in RNA Tertiary Structure Prediction
AI | Antibiotic Resistance | Bioinformatics | Deep Learning | Infectious Diseases | News | Paper Summary | Tools | Trending

Penn Researchers Use Deep Learning to Design Multimodal Antibiotics for Combating Intracellular Infections
ByAnchal Negi January 18, 2025October 22, 2026

The rise of antimicrobial resistance has made treating intracellular infections a significant challenge, as conventional antibiotics often fail to neutralize pathogens hidden within human cells. To address this, researchers from the University of Pennsylvania introduced APEXDUO, an advanced AI model designed to generate peptides with both cell-penetrating and antimicrobial properties. From a vast library of…

Read More Penn Researchers Use Deep Learning to Design Multimodal Antibiotics for Combating Intracellular Infections
AI | Bioinformatics | Deep Learning | News | Paper Summary | Protein Design | Structural Biology | Trending

University of Washington Researchers Revolutionize Beta-Barrel and Transmembrane Nanopore Design Using Deep Learning Tools
ByDeotima Chakraborty January 11, 2025October 22, 2026

A team of researchers from the University of Washington has made significant strides in protein design by addressing long-standing challenges in creating beta-barrel structures. Using advanced deep learning tools, including RoseTTAFold-based design techniques, the team successfully developed transmembrane nanopores with conductances ranging from 200 to 500 pS. This innovative approach combines the precision of parametric…

Read More University of Washington Researchers Revolutionize Beta-Barrel and Transmembrane Nanopore Design Using Deep Learning Tools
Bioinformatics | Biotech | Data Science | Statistics | Trending

10 Days Hands-on Skill Development Training Program on R for Bioinformatics – 16 Jan to 29 Jan 2025
ByCBIRT January 10, 2025October 22, 2026

R is a powerful and versatile programming language widely used in biological data analysis for its robust statistical capabilities and extensive library of tools tailored for data visualization, manipulation, and modeling. In the realm of biology, where datasets are often complex and multidimensional, R provides an efficient platform for analyzing genomic sequences, protein structures, ecological…

Read More 10 Days Hands-on Skill Development Training Program on R for Bioinformatics – 16 Jan to 29 Jan 2025
AI | Bioinformatics | Deep Learning | Drug Discovery | Gene Expression | News | Paper Summary | Tools | Trending

Generating Hit-Like Molecules with Gx2Mol: A Deep Dive into Gene Expression and Drug Discovery
ByDeotima Chakraborty January 10, 2025October 22, 2026

Even though SMILES and molecular graphs have been used in earlier research, drug reactions in biological systems, such as genes and proteins, are frequently disregarded in the de novo production of hit-like compounds. In this study, scientists from Nagoya University and Kyushu Institute of Technology, Japan, provide Gx2Mol, a deep generative model that creates molecular…

Read More Generating Hit-Like Molecules with Gx2Mol: A Deep Dive into Gene Expression and Drug Discovery
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Tools | Trending

Scaling Structure-Based Molecular Design with ECloudGen’s Electron Cloud Approach
ByAnchal Negi January 7, 2025October 22, 2026

In a groundbreaking step toward more effective molecular design, researchers from Zhejiang University and others have introduced ECloudGen, a cutting-edge framework that integrates quantum chemistry with deep learning. This innovative approach focuses on utilizing electron clouds as latent variables to unlock new possibilities in structure-based molecular design. Understanding the Role of Electron Clouds in Chemistry…

Read More Scaling Structure-Based Molecular Design with ECloudGen’s Electron Cloud Approach
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Tools | Trending

MIT Unveils Boltz-1: An Open-Source Tool for Biomolecular Structure Prediction
ByDeotima Chakraborty January 4, 2025October 22, 2026

Developing an understanding of biomolecular interactions is essential for developing domains such as protein design and drug development. The open-source deep learning model Boltz-1, which researchers from MIT present in this research, combines advances in model design, speed optimization, and data processing to predict the 3D structures of biomolecular complexes with an accuracy of AlphaFold3….

Read More MIT Unveils Boltz-1: An Open-Source Tool for Biomolecular Structure Prediction
AI | Bioinformatics | Genomics | News | Trending

Top 5 AI Trends Bioinformaticians Need to Know in 2025
ByDr. Tamanna Anwar December 27, 2024October 22, 2026

As we approach 2025, the integration of artificial intelligence (AI) into bioinformatics is set to transform the field dramatically. Bioinformaticians must stay abreast of several key trends that will shape research methodologies, data analysis, drug discovery, and personalized medicine. Here are the top AI trends to watch that will significantly impact bioinformatics in the coming…

Read More Top 5 AI Trends Bioinformaticians Need to Know in 2025
AI | Bioinformatics | Deep Learning | Molecular Dynamics | News | Paper Summary | Tools | Trending

Predicting Protein Dynamics with BioEmu: A New Frontier in Biomolecular Research
ByDeotima Chakraborty December 24, 2024October 22, 2026

A major field of study is the scientific problem of predicting dynamical mechanisms in proteins after structural revolutions, with molecular dynamics simulations offering important information on conformational states and binding configurations. In this work, researchers from Microsoft Research create a generative deep learning system called Biomolecular Emulator (BioEmu), which can produce thousands of statistically distinct…

Read More Predicting Protein Dynamics with BioEmu: A New Frontier in Biomolecular Research
AI | Bioinformatics | Deep Learning | LLM | Molecular Dynamics | News | Paper Summary | Trending

Harnessing AI for Molecular Discovery: The Promise of Molecular Dynamics Language Models
ByDeotima Chakraborty December 14, 2024October 22, 2026

Although molecular dynamics (MD) simulations provide accurate representations of the motion of molecular systems, molecular biology and materials science are challenged by the computational demands of these simulations. Since deep learning techniques have proven successful in many domains, it is unclear if they can be used to improve the effectiveness of MD simulations. Researchers from…

Read More Harnessing AI for Molecular Discovery: The Promise of Molecular Dynamics Language Models
AI | Bioinformatics | Deep Learning | News | Paper Summary | PLM | Protein Design | Proteomics | Structural Biology | Tools | Trending

Can InterPLM Decode the Hidden Language of Protein Modeling and Design AI?
ByAnchal Negi December 6, 2024October 22, 2026

The emergence of Protein Language Models (PLMs) is changing biological research for the better through efficient prediction of functional annotations and protein structures. However, the actual features and mechanisms of these models still largely remain unclear. This posed a critical question that sparked the curiosity of Elana Simon and James Zou, who are both researchers…

Read More Can InterPLM Decode the Hidden Language of Protein Modeling and Design AI?
AI | Bioinformatics | Deep Learning | News | Paper Summary | Protein Design | Tools | Trending

Learning from Extremophiles: HyperMPNN’s Revolutionary Approach to Thermostable Protein Design
ByDeotima Chakraborty December 6, 2024October 22, 2026

Recombinant proteins must be stable in order to be used in biotechnological or medicinal applications. Both the creation of new proteins and the stabilization of preexisting ones have demonstrated excellent performance using deep-learning protein design techniques such as ProteinMPNN. Nevertheless, given that the natural proteins in the training set are very weakly stable biophysically, it…

Read More Learning from Extremophiles: HyperMPNN’s Revolutionary Approach to Thermostable Protein Design
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending

PharmacoNet: Revolutionizing Ultra-Large-Scale Virtual Screening for Drug Discovery with Deep Learning-Powered Pharmacophore Modeling
ByDeotima Chakraborty November 10, 2024October 22, 2026

Virtual screening techniques, which concentrate on binding affinities, are becoming more and more popular for early-stage drug development. Although deep learning-based methods predict binding affinities, limited training data raises questions about their generalization capabilities in large chemical spaces. In this study, scientists from the Department of Chemistry, KAIST, Republic of Korea, provide PharmacoNet, the first…

Read More PharmacoNet: Revolutionizing Ultra-Large-Scale Virtual Screening for Drug Discovery with Deep Learning-Powered Pharmacophore Modeling
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending

DeltaDock: Streamlining Molecular Docking with a Unified, Reliable Framework
ByAnchal Negi October 26, 2024October 22, 2026

DeltaDock is a novel two-stage framework capable of performing molecular docking, which has not been achieved before. It does so by combining the strengths of geometric deep learning (GDL) methods with reliability associated with traditional sampling approaches. The DeltaDock framework incorporates in its architecture a pocket prediction stage followed by a site-centered docking stage. Researchers…

Read More DeltaDock: Streamlining Molecular Docking with a Unified, Reliable Framework
AI | Bioinformatics | Cancer | Deep Learning | Health | News | Paper Summary | Trending

Deep Learning Models in Pathogenicity Prediction: Insights from Hereditary Breast Cancer Gene Variants in the UK Biobank
ByDeotima Chakraborty October 24, 2024October 22, 2026

Deep learning models have been used to predict the pathogenicity of mutations in hereditary breast cancer gene variants in the UK Biobank. Here, researchers classified germline missense mutations in UK Biobank individuals using three state-of-the-art deep learning models for pathogenicity prediction. The findings demonstrated that missense mutations in BRCA1, BRCA2, and PALB2 were linked to…

Read More Deep Learning Models in Pathogenicity Prediction: Insights from Hereditary Breast Cancer Gene Variants in the UK Biobank
AI | Bioinformatics | Deep Learning | News | Paper Summary | PPI | Structural Biology | Trending

Computing the Human Interactome: A Deep Dive into Protein Interactions
ByAnchal Negi October 16, 2024October 22, 2026

A recent study by a team of researchers has made significant strides in unraveling this complex puzzle of human interaction. The human interactome encompasses all the protein-protein interactions (PPIs) that are essential for protein function and remains an outstanding challenge. It is critical to understand these interactions to understand disease pathology and new effective interventions….

Read More Computing the Human Interactome: A Deep Dive into Protein Interactions
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Trending

Cleveland Clinic and IBM Leverage AI to Revolutionize Drug Discovery for Pain Management
ByDeotima Chakraborty October 11, 2024October 22, 2026

In drug development, it isn’t easy to learn molecular representation from chemical structures since traditional molecular representation techniques, such as sequence-based and graph-based approaches, have limits in extracting useful vectors due to their extensive domain knowledge. Inspired by deep learning technologies based on images and computer vision, researchers have developed a self-supervised framework for learning…

Read More Cleveland Clinic and IBM Leverage AI to Revolutionize Drug Discovery for Pain Management
AI | Bioinformatics | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending

How ChemNet is Revolutionizing Protein-Small Molecule Conformational Modeling
ByDeotima Chakraborty September 30, 2024October 22, 2026

Modeling of protein-small molecule systems offers advantages in speed and generality for exploring interactions with small molecules in the folded state; the modeling of conformational heterogeneity in protein-small molecule systems is deemed an outstanding issue at the atomic level. Researchers from the University of Washington created a graph neural network named ChemNet that was taught…

Read More How ChemNet is Revolutionizing Protein-Small Molecule Conformational Modeling
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Tools | Trending

Unlocking Molecular Dynamics: Dynaformer, a Graph-Based Deep Learning Model for Binding Affinity Prediction
ByCBIRT September 13, 2024October 22, 2026

One major obstacle in structure-based drug design is accurately predicting the affinities of protein-ligand interaction. The accuracy of data-driven affinity prediction methods has improved recently, but its limitations remain. This is partly because these methods rely solely on static crystal structures, whereas thermodynamic ensembles between proteins and ligands typically dictate the real binding affinities. Using…

Read More Unlocking Molecular Dynamics: Dynaformer, a Graph-Based Deep Learning Model for Binding Affinity Prediction
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending

Revolutionary One-Step Approach: Predicting and Screening Protein-Ligand Complexes with Geometric Deep Learning
ByDeotima Chakraborty August 29, 2024October 22, 2026

Deep learning is being utilized in the course of drug development to understand the structure of the protein-ligand complex, utilizing the technique of virtual structure measurement that combines docking and deep learning despite limitations in accuracy and efficiency. Here, researchers demonstrate that a single multi-task geometric deep learning model, LigPose, can accurately handle these two…

Read More Revolutionary One-Step Approach: Predicting and Screening Protein-Ligand Complexes with Geometric Deep Learning
AI | Bioengineering | Bioinformatics | Deep Learning | News | Paper Summary | Tools | Trending

DeepEnzyme Improves Enzyme Turnover Prediction Accuracy Leveraging Deep Learning and Protein 3D-Structure Features
ByDeotima Chakraborty August 27, 2024October 22, 2026

In synthetic biology, turnover numbers (kcat) — a critical measure of an enzyme’s efficiency—have a variety of applications. On the other hand, kcat measurement of enzymes takes time. Although prediction accuracy has increased thanks to deep learning models, more advancements are still required, particularly for enzymes whose sequences differ significantly from those in the training…

Read More DeepEnzyme Improves Enzyme Turnover Prediction Accuracy Leveraging Deep Learning and Protein 3D-Structure Features
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 | Machine Learning | News | Paper Summary | PLM | Proteomics | Tools | Trending | Web Servers

No Coding Required! SaprotHub Brings Protein Modeling to Every Biologist
ByDeotima Chakraborty June 4, 2024October 22, 2026

Users without prior Machine Learning (ML) experience have difficulties when it comes to training and implementing deep learning models. Regardless of prior experience with machine learning, SaprotHub, introduced by the researchers of Westlake University, provides a simple platform for training, using, and exchanging protein models, encouraging cooperation among biologists. All it takes is a few…

Read More No Coding Required! SaprotHub Brings Protein Modeling to Every Biologist
AI | Bioinformatics | Drug Discovery | News | Paper Summary | Structural Biology | Tools | Trending

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 | 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
AI | Bioinformatics | Deep Learning | News | OMICS | Paper Summary | PPI | Proteomics | Structural Biology | Tools | Trending

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 | AMR Alert | Antibiotic Resistance | Bioinformatics | Deep Learning | Infectious Diseases | LLM | News | Paper Summary | Proteomics | Trending

Turning the Tide Against Antibiotic Resistance: Protein-BERT’s Accurate and Interpretable Predictions
ByAnchal Negi May 13, 2024October 22, 2026

Scientists from Waseda University and Hitachi, Ltd., Japan, developed a novel approach for predicting antibiotic resistance genes (ARGs) resistance mechanisms using Protein-BERT, a protein language model based on deep learning. This model excels at predicting resistance for genes with low similarity to known sequences. By analyzing biologically relevant features like conserved residues and binding sites,…

Read More Turning the Tide Against Antibiotic Resistance: Protein-BERT’s Accurate and Interpretable Predictions
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 | Gene Expression | Genomics | News | Paper Summary | Trending

From Genome to Function: AI Predicts Gene Regulation in Newly Sequenced Plants
ByAnshika Gupta May 3, 2024October 22, 2026

A recent study published in Nature Communications explored the relationship between gene expression prediction and the use of deep learning models, which are the most advanced models or architectures in the domain of machine learning. Fritz Forbang Peleke and Jędrzej Szymański head the group to uncover the role of non-coding regulatory elements in gene expression…

Read More From Genome to Function: AI Predicts Gene Regulation in Newly Sequenced Plants
AI | Bioengineering | Bioinformatics | Deep Learning | LLM | News | Paper Summary | Trending

UC Berkeley’s Breakthrough RNA Language Models Predict Mutations that Enhance RNA Function
ByAnchal Negi April 15, 2024October 22, 2026

A new tool from UC Berkeley can predict mutations that can improve the way how RNA works. By leveraging hyperthermophilic RNAs, the researchers identified mutations conferring increased thermostability to E. coli ribosomes, providing a foundation to understand RNA sequence-structure-function relationships. Introduction Do you remember what DNA is? It’s that molecule that contains all the information…

Read More UC Berkeley’s Breakthrough RNA Language Models Predict Mutations that Enhance RNA Function
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Paper Summary | Tools | Trending

Bridging Gaps in Structure-Based Drug Design: The MolSnapper’s Breakthrough Conditioning Diffusion Approach
ByAnshika Gupta April 3, 2024October 22, 2026

The synergy among the Nuffield Department of Medicine and the Department of Statistics at the University of Oxford represents a blend of medical expertise with statistical rigor – “The MolSnapper,” built using deep learning, promptly exploring intricate molecular specificity for proteins in 3D conformations. Unlike previous approaches that rely on costly trial and error, Conditioning…

Read More Bridging Gaps in Structure-Based Drug Design: The MolSnapper’s Breakthrough Conditioning Diffusion Approach
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Tools | Trending

Basecamp Research Introduces BaseFold: A New Deep Learning Approach to Protein Structure Prediction
ByDeotima Chakraborty March 17, 2024October 22, 2026

Scaling laws predict the presence of over a trillion species on our planet. However, only a small fraction of them have been studied. Deep learning models applied for work in life sciences generally depend on the size and quality of training or reference datasets. The study suggests a data-centric strategy to improve deep learning models…

Read More Basecamp Research Introduces BaseFold: A New Deep Learning Approach to Protein Structure Prediction
AI | Bioinformatics | Deep Learning | News | Paper Summary | Structural Biology | Trending

Exploring the Boundless Frontiers of Biomolecular Modeling and Design with RoseTTAFold All-Atom
ByDeotima Chakraborty March 14, 2024October 22, 2026

Models like AlphaFold2 (AF2) and RoseTTAFold, a modified structural biology tool, have not been able to replicate well the interactions or covalent changes with tiny molecules that are essential to biological activity. Here, researchers from the University of Washington describe RoseTTAFold All-Atom (RFAA), a deep network that, given the sequences of the polymers and the…

Read More Exploring the Boundless Frontiers of Biomolecular Modeling and Design with RoseTTAFold All-Atom
AI | Bioinformatics | Deep Learning | Drug Discovery | News | Structural Biology | Trending

PocketGen: A Novel Deep Generative Model for Designing Protein Binding Sites
ByDr. Tamanna Anwar March 1, 2024October 22, 2026

The ability to accurately model and redesign ligand binding sites (pockets) on protein surfaces holds tremendous value across fields like drug discovery, enzyme engineering, and biosensing. However, effectively generating protein pockets tailored to recognize user-defined small molecules has remained challenging. Recent research from the University of Science and Technology of China details PocketGen – an…

Read More PocketGen: A Novel Deep Generative Model for Designing Protein Binding Sites
AI | Bioinformatics | Deep Learning | News | Paper Summary | Sequencing | Trending

Unlock the Potential of DeepMod2: A Deep Learning Framework for Precise DNA Methylation Analysis using Oxford Nanopore Sequencing
ByDeotima Chakraborty February 27, 2024October 22, 2026

Reduced representation methylation sequencing for CpG islands or imprinted areas is made possible by Oxford Nanopore sequencing, which offers a distinct advantage over other techniques by identifying DNA methylations from the ionic current signal of individual molecules. Here, scientists present DeepMod2, a thorough deep-learning framework that uses the ionic current signal from Nanopore sequencing to…

Read More Unlock the Potential of DeepMod2: A Deep Learning Framework for Precise DNA Methylation Analysis using Oxford Nanopore Sequencing

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