Drug Discovery

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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
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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
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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
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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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Smart Sampling for Smarter Drugs: How Active Learning Boosts Drug Discovery
ByDeotima Chakraborty April 16, 2024October 22, 2026

In computational drug discovery, active learning (AL) is a potent approach that makes it possible to identify the best binders from large chemical libraries. To get the best results, designing a strong AL methodology necessitates an understanding of AL parameters and data set properties. In order to systematically assess the efficacy of machine learning models…

Read More Smart Sampling for Smarter Drugs: How Active Learning Boosts Drug Discovery
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Meet MolProphet: A One-Stop AI Platform for the Early Stages of Drug Discovery
ByAnshika Gupta April 5, 2024October 22, 2026

Are you aware that plenty of operational AI applications have emerged in the early stages of small-molecule drug discovery? Do you recognize that those packages regularly require a thorough grasp of software programs and hardware, as well as a specialization in an optimistic effort, inflicting boundaries for regular users, in fact, normalization and switching among…

Read More Meet MolProphet: A One-Stop AI Platform for the Early Stages of Drug Discovery
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Can AI Help Us Discover New Drugs Faster? Unveiling MegaKG, the Explainable Knowledge Graph
ByAnchal Negi April 4, 2024October 22, 2026

Drug discovery has traditionally been a tedious and difficult process characterized by sifting through large amounts of data, which may be about to get a disruptive roadmap. A new research study by researchers from MegaRobo Technologies, China, describes MegaKG, an enormous knowledge graph merging information from twenty-three different sources. This AI-powered KG does not just…

Read More Can AI Help Us Discover New Drugs Faster? Unveiling MegaKG, the Explainable Knowledge Graph
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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
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Exploring Medicine’s Future with KnowDDI: How Knowledge Subgraph Learning Improves Drug-Drug Interaction Prediction
ByAnchal Negi April 1, 2024October 22, 2026

Have you ever wondered what happens when you take multiple medications? Although each drug is intended to address a particular condition, there are instances whereby their cumulative effects may be erratic and dangerous. This is where DDI science comes in. In this post, we will expand on an advanced technique called KnowDDI, developed by Tsinghua…

Read More Exploring Medicine’s Future with KnowDDI: How Knowledge Subgraph Learning Improves Drug-Drug Interaction Prediction
Bioinformatics | Infectious Diseases | News | Paper Summary | Trending

A New Weapon in the Public Health Arsenal: VaccineDesigner Simplifies Epitope-based Vaccine Design
ByAnshika Gupta April 1, 2024October 22, 2026

A ground-breaking solution in modern-day continuously advancing healthcare internationally is developing waves in vaccine improvement. Developed by brilliant minds at the School of Medicine, Aristotle University of Thessaloniki, VaccineDesigner affords a powerful platform for epitope-based vaccine layout. It’s poised to convert how we tackle infectious diseases and address worldwide health challenges. Unveiling VaccineDesigner VaccineDesigner is a pioneering…

Read More A New Weapon in the Public Health Arsenal: VaccineDesigner Simplifies Epitope-based Vaccine Design
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Meet PiMine: A Database-Driven Approach for Protein-Protein Interface Similarity Detection
ByAnshika Gupta March 30, 2024October 22, 2026

Researchers at Universität Hamburg introduced PiMine, an application that senses structurally related protein-protein interfaces while being driven by a database. PiMine analyzes these interfaces, which are sources of information about protein function and are also important in drug discovery. It distinguishes itself from other programs by the ability to scrutinize interfaces in terms of structural…

Read More Meet PiMine: A Database-Driven Approach for Protein-Protein Interface Similarity Detection
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Brown University’s Subsampled AlphaFold2 Approach for Predicting Protein Dynamics: A Game-changer for Drug Discovery?
ByAnchal Negi March 30, 2024October 22, 2026

Proteins are life’s molecular workhorses and play crucial roles in every biological process. Understandably, their complex shapes, also known as conformations, are the key that unlocks their secrets to new drug development. Proteins are not still statues but rather change into different conformations like ninjas to play different functions. This movement of protein has been…

Read More Brown University’s Subsampled AlphaFold2 Approach for Predicting Protein Dynamics: A Game-changer for Drug Discovery?
Bioinformatics | Chemoinformatics | Computational Models | Drug Discovery | News | Paper Summary | Protein Design | Structural Biology | Tools | Trending

Unlocking New Drug Targets: Researchers Develop Computational Method to Target Proteins Inside Cell Membranes
ByAnchal Negi March 28, 2024October 22, 2026

Can you imagine that you can design proteins that have a particular function, and they are like small robots working inside our cells? This may be the reality pretty soon. Researchers from the University of California and Yale University came up with an innovative way of creating transmembrane proteins from scratch, thus leading to targeted…

Read More Unlocking New Drug Targets: Researchers Develop Computational Method to Target Proteins Inside Cell Membranes
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AI vs. Superbugs: A New Generative AI Model ‘SyntheMol’ Designs Powerful Antibiotics to Fight Resistant Bacteria
ByDeotima Chakraborty March 25, 2024October 22, 2026

AI is being used to find novel antibiotics for bacteria resistant to pandemics; however, the currently used techniques have serious drawbacks. While generative models design molecules—which are difficult to synthesize—property prediction models, which assess molecules individually, have difficulty scaling to enormous chemical spaces. The generative model SyntheMol, which creates readily synthesizable chemicals from a chemical…

Read More AI vs. Superbugs: A New Generative AI Model ‘SyntheMol’ Designs Powerful Antibiotics to Fight Resistant Bacteria
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Microsoft Reveals ViSNet: A Cutting-Edge Framework for Molecular Property Prediction and Simulating Molecular Dynamics
ByAnchal Negi March 7, 2024October 22, 2026

Molecular geometry modeling, which involves building models that show the spatial arrangement of atoms in a molecule, usually helps with this understanding. The field of molecular modeling has been transformed by geometric deep learning. However, due to molecules’ inherent 3D shape and the intricate interactions between their constituent atoms, standard deep learning algorithms need help…

Read More Microsoft Reveals ViSNet: A Cutting-Edge Framework for Molecular Property Prediction and Simulating Molecular Dynamics
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Eurofins Discovery Launches DiscoveryAI SAFIRE: A New AI Platform for Drug Discovery
ByDr. Tamanna Anwar March 3, 2024October 22, 2026

The drug discovery process is unduly time-consuming and expensive. Extensive laboratory testing is required to evaluate potential drug candidates for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Compounds that fail to meet acceptable thresholds for these properties may be unsafe or ineffective in humans, leading to high failure rates in clinical trials. But what if…

Read More Eurofins Discovery Launches DiscoveryAI SAFIRE: A New AI Platform for Drug Discovery
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
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Unlocking New Frontiers in Drug Design with PCMol: Integrating AlphaFold and De Novo Approaches
ByDeotima Chakraborty February 24, 2024October 22, 2026

Deep learning-based virtual screening provides a more effective way to find molecules that resemble drugs, and virtual sources give chemists useful information. Leiden University researchers present PCMol, a multi-target model that trains the de novo generative model on target proteins using the latent embeddings extracted from AlphaFold. Protein descriptors are a useful tactic for expanding…

Read More Unlocking New Frontiers in Drug Design with PCMol: Integrating AlphaFold and De Novo Approaches
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Cracking the Code of Drug-Drug Interaction: DDIPrompt Unlocks a World of Safer Treatments
ByDr. Tamanna Anwar February 22, 2024October 22, 2026

Adverse drug-drug interactions (DDIs) pose a major health risk when using drug combinations to treat complex diseases. However, predicting harmful DDIs is extremely challenging due to the intricacies of modeling interactions between drug molecules. In a new preprint posted on arXiv, scientists from Fudan University, The Chinese University of Hong Kong, and the University of…

Read More Cracking the Code of Drug-Drug Interaction: DDIPrompt Unlocks a World of Safer Treatments
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Revolutionizing Drug Discovery with ChemGLaM: Chemical-Genomics Language Models for Compound-Protein Interaction Prediction
ByDeotima Chakraborty February 21, 2024October 22, 2026

Compound-protein interaction (CPI) prediction accuracy is crucial for drug discovery. Creating deep learning models that can be applied broadly requires expanding the CPI data through experimental validation. With their strong generalization performance in a range of tasks involving substances and proteins, large language models (LLMs) such as chemical and protein language models have become foundational…

Read More Revolutionizing Drug Discovery with ChemGLaM: Chemical-Genomics Language Models for Compound-Protein Interaction Prediction
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OpenFold Unveils SoloSeq and OpenFold-Multimer: Advancing AI in Protein Structure Analysis
ByDr. Tamanna Anwar February 20, 2024October 22, 2026

OpenFold, an artificial intelligence (AI) research consortium, has announced the release of two new tools that aim to improve protein structure prediction: SoloSeq and OpenFold-Multimer. These open-source tools represent important advances in integrating protein language models with structure prediction software, as well as modeling protein complexes more accurately. The SoloSeq model developed on Amazon Web…

Read More OpenFold Unveils SoloSeq and OpenFold-Multimer: Advancing AI in Protein Structure Analysis
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University of Michigan Researchers Discover Novel Plant Protein Fold with Potential for Anti-Cancer Drugs
ByDr. Tamanna Anwar February 16, 2024October 22, 2026

Researchers from the University of Michigan have reported the discovery of a new plant protein fold that catalyzes the formation of macrocyclic peptides through a novel mechanism. Their findings provide insight into nature’s peptide cyclization strategies and expand the toolbox for bioengineering cyclic peptides as potential therapeutics. Introduction Cyclic peptides have gained increasing interest as…

Read More University of Michigan Researchers Discover Novel Plant Protein Fold with Potential for Anti-Cancer Drugs
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Meet Proteus: Transforming the Landscape of Protein Design with Innovation and Efficiency
ByDr. Tamanna Anwar February 16, 2024October 22, 2026

Proteins perform essential functions in living organisms and adopt specific three-dimensional structures dictated by their amino acid sequences. The ability to accurately design novel protein structures from scratch could enable advances in areas like protein engineering and drug development. Recently, deep learning techniques like diffusion models have shown promise for generating designable protein structures without…

Read More Meet Proteus: Transforming the Landscape of Protein Design with Innovation and Efficiency
AI | Bioinformatics | Computational Models | Drug Discovery | LLM | News | Proteomics | Trending

GPCR-BERT: Revolutionizing GPCR Research with Protein Language Models
ByDr. Tamanna Anwar February 13, 2024October 22, 2026

Recent advances in large language models (LLMs) and transformers have opened new possibilities for modeling protein sequences as language. Carnegie Mellon University researchers have developed a new AI model called GPCR-BERT that leverages advances in natural language processing to uncover new insights into the sequence-function relationships in G protein-coupled receptors (GPCR), which are the target…

Read More GPCR-BERT: Revolutionizing GPCR Research with Protein Language Models
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tFold Unveiled: A Swift and Accurate Approach to Antibody-Antigen Complex Modeling and Design
ByAnchal Negi February 13, 2024October 22, 2026

The soldiers of our immune system, antibodies, bind to specific targets like viruses and toxins and neutralize them. Predicting antibody-antigen complex structures accurately has the potential to significantly advance both the creation of therapeutic antibodies and scientific research. Traditional approaches are costly and slow, but an emerging technique called tFold (tFold-Ab and tFold-Ag) is rewriting…

Read More tFold Unveiled: A Swift and Accurate Approach to Antibody-Antigen Complex Modeling and Design
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Making AI-Driven Structural Biology Accessible with Foldy
ByDr. Tamanna Anwar February 11, 2024October 22, 2026

Recent breakthroughs in AI-based protein structure prediction have enormous potential to accelerate discoveries across the life sciences. However, major barriers persist in making these advanced tools accessible to non-experts without specialized computing resources. To address this gap, researchers from Joint BioEnergy Institute, US, have developed Foldy – an intuitive web application that puts powerful structural…

Read More Making AI-Driven Structural Biology Accessible with Foldy
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Unleashing nanoBERT: Revolutionizing Nanobody Therapeutics with Deep Learning
ByDr. Tamanna Anwar February 9, 2024October 22, 2026

Scientists from the University of Southern Denmark, NaturalAntibody and Alector Therapeutics unveil nanoBERT, a nanobody-specific transformer designed to predict amino acids in specific positions within query sequences. NanoBERT outperforms human-based language models and ESM-2, demonstrating the benefits of domain-specific language modeling. Fine-tuning on thermostability datasets shows promise for engineering therapeutic nanobodies. Nanobodies, also known as…

Read More Unleashing nanoBERT: Revolutionizing Nanobody Therapeutics with Deep Learning
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Revolutionizing Protein-protein Interaction Prediction with LazyAF: A Breakthrough Pipeline for Medium-scale In Silico Analysis
ByDeotima Chakraborty February 5, 2024October 22, 2026

Predicting protein structures has been transformed by artificial intelligence. However, rather than competence, accessibility, and ease of use are increasingly becoming limiting issues for end users as more sophisticated and powerful software is developed. In order to simplify the process of medium-scale protein-protein interaction prediction, the author Thomas C. McLean, from John Innes Centre, UK, presents…

Read More Revolutionizing Protein-protein Interaction Prediction with LazyAF: A Breakthrough Pipeline for Medium-scale In Silico Analysis
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Meet ChatDrug: A New ChatGPT-powered Conversational Drug Editing Framework
ByDeotima Chakraborty January 23, 2024October 22, 2026

Drug discovery is one of the many fields where recent developments in conversational large language models (LLMs), like ChatGPT, have shown incredible promise. However, the majority of the work that has already been done is on examining conversational LLMs’ skills with regard to chemical reactions and retrosynthesis. However, there is still much to learn about…

Read More Meet ChatDrug: A New ChatGPT-powered Conversational Drug Editing Framework
Bioinformatics | Cancer | Cancer Research | News | Paper Summary | Proteomics | Structural Biology | Trending

The Glue that Heals: St. Jude’s Researchers Unveil Molecular ‘Super-Glue’ Propelling Innovative Cancer Drug Development
ByAnchal Negi January 20, 2024January 21, 2024

Scientists at St. Jude Children’s Research Hospital have published their findings on SJ3149, a chemical with broad efficacy against various cancer types, particularly acute myeloid leukemia (AML). Scientists worked together to discover and improve a chemical molecule that functions as a molecular “super-glue” for a variety of tumors. SJ3149 binds to cancer-related protein casein kinase…

Read More The Glue that Heals: St. Jude’s Researchers Unveil Molecular ‘Super-Glue’ Propelling Innovative Cancer Drug Development
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Mapping Cancer’s Weaknesses: A Comprehensive Map of Cancer Cell Dependencies Accelerates Drug Discovery
ByAnchal Negi January 18, 2024October 22, 2026

Cancer, a simple name for a complicated group of diseases, has long perplexed scientists and healthcare professionals alike. Despite years of research and medical advances, individualized therapy remains elusive. However, a recent study using multi-omics data is bringing us closer to understanding cancer’s weaknesses and maybe opening up new therapeutic options. By integrating diverse datasets…

Read More Mapping Cancer’s Weaknesses: A Comprehensive Map of Cancer Cell Dependencies Accelerates Drug Discovery
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Language Models Paired with Geometric Deep Learning Revolutionize Genome-scale Protein Binding Site Annotation
ByDeotima Chakraborty January 17, 2024October 22, 2026

Protein structures depend on nucleic acids and tiny ligands to establish binding sites. Accurate identification of these locations is necessary due to the exponential increase of protein sequence databases. However, the current approaches do not thoroughly address the geometry of protein structures and limit genome-scale applications by relying on costly multiple sequence alignments or experimental…

Read More Language Models Paired with Geometric Deep Learning Revolutionize Genome-scale Protein Binding Site Annotation
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High-throughput ADMET Properties Prediction Made Easy with ADMET-AI: A New Platform for Large-scale Chemical Libraries Evaluation
ByAnchal Negi January 13, 2024October 22, 2026

A vital step in drug discovery is accurately evaluating potential drug candidates’ Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). Traditionally, this process has been time-consuming, expensive, and even limited to small compound libraries, making it difficult to test larger libraries. Recently, a group of researchers developed ADMET-AI, a simple, fast, and accurate platform for ADMET…

Read More High-throughput ADMET Properties Prediction Made Easy with ADMET-AI: A New Platform for Large-scale Chemical Libraries Evaluation
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AlphaFold’s Latest Strides: Improved Accuracy for Antibody-Antigen Complex Modeling
BySonal Keni January 5, 2024January 5, 2024

The launch of AlphaFold signaled a revolution in computational modeling: armed with new knowledge of protein structures and unencumbered by limitations on resources and time, scientists were able to use the tool to further their understanding of protein structure, functionality, and interactions. However, AlphaFold’s success in the realm of antibody-antigen modeling is limited. A new…

Read More AlphaFold’s Latest Strides: Improved Accuracy for Antibody-Antigen Complex Modeling
Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending

Unleashing MACE-OFF23: Machine Learning Empowers Transferable Force Field for Organic Molecules
ByDeotima Chakraborty January 4, 2024October 22, 2026

For more than fifty years, biomolecular simulation has been dominated by classical empirical force fields. Despite being extensively employed in biomolecular dynamics, crystal structure prediction, and drug discovery, they typically lack the precision and transferability necessary for predictive modeling. In this paper, researchers from the University of Cambridge introduced MACE-OFF23. A high degree of quantum…

Read More Unleashing MACE-OFF23: Machine Learning Empowers Transferable Force Field for Organic Molecules
AI | Antibiotic Resistance | Bioinformatics | Deep Learning | Drug Discovery | Infectious Diseases | News | Paper Summary | Trending

MIT Researchers Use AI to Design New Class of Antibiotic Candidates in the Fight Against Superbugs
BySonal Keni December 26, 2023January 4, 2024

Since the discovery of penicillin, a large number of antibiotics have been developed and used all over the world as a means to cure disease. While this has been a boon for humanity, allowing for the easy, effective, and cheap treatment of microbial disease, its misuse has also resulted in many pathogens developing resistance to…

Read More MIT Researchers Use AI to Design New Class of Antibiotic Candidates in the Fight Against Superbugs
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A GPT-driven Robot Chemist Designs, Plans, and Performs Complex Experiments – All by Itself!
ByDr. Tamanna Anwar December 21, 2023October 22, 2026

Scientists at Carnegie Mellon University have unveiled a breakthrough artificial intelligence system called Coscientist that can independently design and execute complex chemistry experiments at the touch of a button. The system aims to accelerate discoveries across scientific domains by leveraging the raw reasoning power of models like GPT-4. Coscientist combines natural language processing prowess with tools enabling…

Read More A GPT-driven Robot Chemist Designs, Plans, and Performs Complex Experiments – All by Itself!
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Decoding the Dance of Proteins and Nucleic Acids: Machine Learning Empowers Protein-Nucleic Acid Complexes Prediction with RoseTTAFoldNA
ByDeotima Chakraborty December 3, 2023January 26, 2024

Researchers at the University of Washington, USA, have developed a new AI system called RoseTTAFoldNA that can rapidly and accurately predict the 3D structures of proteins bound to DNA and RNA. The ability to model these protein-nucleic acid complexes has important implications across fields from biomedicine to biotechnology. RoseTTAFoldNA expands on the award-winning protein structure…

Read More Decoding the Dance of Proteins and Nucleic Acids: Machine Learning Empowers Protein-Nucleic Acid Complexes Prediction with RoseTTAFoldNA
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Transforming Fragment-based Drug Design: Stanford’s FRAME Employs Machine Learning to Expand Fragments into Ideal Ligands
BySwasti Tyagi December 1, 2023October 22, 2026

Stanford University researchers developed FRAME (Fragment-Based Molecular Expansion), a machine learning framework that uses three-dimensional structures of interactions between proteins and ligands to facilitate drug design based on ‘fragments.’ Fragments are singular chemical groups that are added to ligands for the purpose of performing lead optimization tasks and improving their properties. Ligands are small molecules…

Read More Transforming Fragment-based Drug Design: Stanford’s FRAME Employs Machine Learning to Expand Fragments into Ideal Ligands
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Decoding the Protein-Ligand Enigma: A Deep Dive into Deep Learning Approaches for Binding Affinity Prediction
ByDeotima Chakraborty November 30, 2023November 30, 2023

Researchers from the University of Edinburgh, UK, explore the impact of ligand and protein encodings on binding affinities prediction for different data sets, a crucial aspect of drug development and discovery. Sequence-based deep learning, convolutional neural network-based encodings, and graph neural network-based encodings were used to investigate protein roles. Graph-neural networks were used to construct…

Read More Decoding the Protein-Ligand Enigma: A Deep Dive into Deep Learning Approaches for Binding Affinity Prediction
Bioinformatics | CRISPR | Drug Discovery | Genetic Engineering | News | Precision Medicine

UK Approves Revolutionary World-first Gene Editing Treatment for Blood Disorders
ByDr. Tamanna Anwar November 23, 2023October 22, 2026

The UK has achieved a historic milestone by becoming the first country to authorize a revolutionary new treatment for two serious inherited blood conditions – sickle cell disease and beta-thalassemia. The cutting-edge therapy utilizes CRISPR gene editing technology to precisely correct errors in patients’ blood stem cells, potentially curing them of debilitating symptoms. Announced earlier…

Read More UK Approves Revolutionary World-first Gene Editing Treatment for Blood Disorders
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Deep Learning Unveils Antimalarial Drug’s Potential to Combat Osteoporosis
BySonal Keni October 24, 2023January 10, 2024

Scientists at Peking University School and Hospital for Stomatology, China, have used deep learning to identify dihydroartemisinin (DHA), an antimalarial drug, as a potential treatment for osteoporosis. DHA was found to maintain the stemness of mesenchymal stem cells (MSCs), which are important for bone formation. In mice, DHA treatment was able to reverse osteoporosis-related bone…

Read More Deep Learning Unveils Antimalarial Drug’s Potential to Combat Osteoporosis
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Pharmacophore-Guided Deep Learning: A New Paradigm for Generating Bioactive Molecules in Drug Discovery
BySwasti Tyagi October 15, 2023October 15, 2023

One of the biggest challenges to drug discovery is designing molecules that express desired biological activity, also referred to as bioactivity. This is of great importance when intending to use the drug for treating diseases that have not been thoroughly researched. To tackle this, researchers from Central South University, China, developed PGMG, which stands for…

Read More Pharmacophore-Guided Deep Learning: A New Paradigm for Generating Bioactive Molecules in Drug Discovery
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Variational Auto-encoders for Ligand Generation: A New Approach to Drug Discovery
ByPrachi Khare October 13, 2023October 22, 2026

The search for molecules that bind effectively to specific proteins is crucial from a drug discovery point of view. The existing methods for creating these chemicals are time-consuming and costly. In this study, TargetVAE, a one-of-a-kind AI model developed by researchers from Indiana State University, is utilized to generate potential drug-like molecules with high binding…

Read More Variational Auto-encoders for Ligand Generation: A New Approach to Drug Discovery
AI | Bioinformatics | Cell | Computational Models | Deep Learning | Drug Discovery | News | NGS | Paper Summary | RNA-Seq | Trending

GeneCompass: A New Foundation Model that Unlocks the Secrets of Gene Regulation Across Species
BySwasti Tyagi October 4, 2023December 7, 2023

A team of researchers from the University of Chinese Academy of Sciences, China, and collaborators developed GeneCompass, one of the first foundation models of its kind that encompasses a vast expanse of knowledge across a diverse array of species, owing to the fact that it has been trained on over 120 million single-cell transcriptomes derived…

Read More GeneCompass: A New Foundation Model that Unlocks the Secrets of Gene Regulation Across Species
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MitoSpace: A Novel Mitochondrial Phenotypic Atlas for Drug Discovery and Diagnostics
BySwasti Tyagi September 27, 2023October 22, 2026

A collaborative effort between the Departments of Pharmacology, Chemistry, and Biochemistry at the University of California, San Diego, led to the development of MitoSpace, a self-supervised deep learning model. It can be considered an ‘atlas’ to map various phenotypic characteristics of the mitochondria’s reactions and responses to different drugs. It is a high-content image-based screening…

Read More MitoSpace: A Novel Mitochondrial Phenotypic Atlas for Drug Discovery and Diagnostics
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DrugChat: A ChatGPT-like System for Drug Compound Discovery
BySwasti Tyagi September 24, 2023January 23, 2024

A team of researchers at the University of California, San Diego, came together to develop a chatbot specifically catered to drug discovery called DrugChat. It is similar to ChatGPT on the basis that users can give question prompts to the model on any drug compound under study after uploading the drug’s molecular graph and can…

Read More DrugChat: A ChatGPT-like System for Drug Compound Discovery
AI | Bioinformatics | COVID-19 | Covid-19 News | Drug Discovery | Machine Learning | News | Proteomics | SARS-CoV-2 | Structural Biology | Trending

AI-Powered Protein-Protein Interaction Drug Discovery Pipeline Yields a Promising SARS-CoV-2 Inhibitor
ByAditi Sarkar June 24, 2023June 24, 2023

The multi-adaptive Support Vector Machine Learning Algorithm (maSVM) can improve comparisons between binary Protein-Protein interaction datasets gathered in different laboratories and can be utilized to confidently prioritize potential targets for Protein-Protein Interaction (PPI) based drug discovery. The researchers first showed that the automatic classification of large quantitative interaction datasets could be performed with high confidence….

Read More AI-Powered Protein-Protein Interaction Drug Discovery Pipeline Yields a Promising SARS-CoV-2 Inhibitor
Bioinformatics | Drug Discovery | Machine Learning | Network Modeling | Neural Network | News | Paper Summary | Trending

Scientists Develop a Novel Probabilistic Generative Model ‘RaptGen’ for Aptamer Generation
ByDr. Tamanna Anwar June 10, 2022October 22, 2026

Scientists from the Graduate School of Advanced Science and Engineering at Waseda University, Japan, developed RaptGen, a variational autoencoder for in silico aptamer generation. RaptGen takes advantage of a profile hidden Markov model decoder to address motif sequences efficiently. The model could accelerate nucleic-acid-based therapeutics. Nucleic acid aptamers are created by an in vitro molecular evolution…

Read More Scientists Develop a Novel Probabilistic Generative Model ‘RaptGen’ for Aptamer Generation
Bioinformatics | Corona Virus | COVID-19 | Covid-19 News | Drug Discovery | News | Structural Biology | Trending

Scientists Develop Hybrid Molecules by Drug Repurposing in an Effort to Design New Drugs to Treat Covid-19
ByDr. Tamanna Anwar May 26, 2022October 22, 2026

Controlling the spread of the highly contagious disease and protecting human health requires developing new medications that stay effective against growing COVID-19 variations. After spending more than two years studying SARS-CoV-2, the virus that causes COVID-19, the researchers at the Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) are designing and testing small-molecule antivirals…

Read More Scientists Develop Hybrid Molecules by Drug Repurposing in an Effort to Design New Drugs to Treat Covid-19

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