Harvard Apparatus’ Post

New Research Highlight! 📓 https://lnkd.in/gJ2UhxJs Over the last decade, there has been increasing interest in novel therapuetic modalities using lipid nanoparticle (LNP) delivery systems for drugs. Common LNP formulations contain four types of lipids, with one, the ionizable lipid, having dual packaging and release functions for the RNA cargo. However, the specific chemistry and structure of any single ionizable lipid is not broadly generalizable across applications. There is a growing need for a deeper pool of these lipids to choose from as new RNA-based applications are developed. Recent work by Bowen Li and colleagues at the University of Toronto demonstrates how deep learning tools can be utilized to develop new ionizable lipids with their platform, AI-Guided Ionizable Lipid Engineering (AGILE). Read the full article here: https://lnkd.in/g7QpYm3G Echelon has been Accelerating your Discovery since 1997, and is proud to have provided ionizable lipids for this research. Congratulations to Li and team on this important work! #IonizableLipids #LipidNanoparticles #AI

Is AI paving the way for a new era of drug discovery? Recently, as a part of my biochemistry coursework, we were tasked with investigating and presenting a newly discovered biomolecule within last five years. Through extensive research, I came across an intriguing antibiotic compound called Halicin, which interestingly, has emerged from the realm of AI-driven drug prediction! They showed how finding new antibiotics can be facilitated by combining in- Silico predictions with empirical research. They take a three-stage approach: 1. Training deep neural network model to predict inhibition of Escherichia coli using a library of 2,335 chemicals. 2. Applying the obtained model to several separate chemical libraries, including >107 million molecules, to discover prospective lead compounds with efficacy against E.coli. 3. Choosing a candidate according to availability, chemical structure, and a predetermined prediction score threshold. Halicin portrayed remarkable efficacy against a spectrum of pathogens, and shows promising role in our fight against antibiotic-resistant infections. The way that cutting-edge technology is transforming drug development is really intriguing, and the peculiar genesis of halicin highlights how AI has the potential to provide ground-breaking medical breakthroughs. Link to the paper: https://lnkd.in/dYxe67_r read more about Halicin: https://lnkd.in/dhhYMSp5 #science #technology #antibiotic #AI

🌟 Exciting advancements in protein design! 🧬 An international team, including experts from the Technical University of Munich and MIT, has leveraged AI to revolutionize the design of artificial proteins. By combining AlphaFold2's cutting-edge structure prediction capabilities with a gradient descent approach, they have developed a groundbreaking method to create large proteins with precise properties. The new technique enables the crafting of proteins to perform specific functions, such as binding to other proteins or delivering drugs. Through iterative optimization, the team successfully designed over 100 proteins, achieving remarkable accuracy between predicted and actual structures in laboratory tests. This innovation not only excels in accuracy but also scales up protein size near those of antibodies, potentially embedding multiple functions to combat pathogens. With such capabilities, the frontier of synthetic biology is expanding, holding promise for future therapeutic and diagnostic applications. #ProteinDesign #Biotechnology #AI #Innovation #Healthcare

🔬 Delighted to unveil our latest research article: "Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks"! 🌟 This project epitomizes the synergy between pharmaceutical science and cutting-edge technology. Months of diligent effort culminated in leveraging the power of artificial neural networks to predict antimicrobial activity. 💡 The integration of machine learning not only propelled our research forward but also underscores the potential for innovation in drug discovery. The invaluable feedback from reviewers further refined our model, enhancing its accuracy and reliability. 🚀 Excited to contribute to the frontier of predictive modeling in pharmaceuticals, advancing the quest for more effective antimicrobial agents. #Research #PharmaceuticalResearch #MachineLearning #NeuralNetworks #AntimicrobialActivity #Innovation #Collaboration

"Protein design aims to create customized antibodies for therapies, biosensors for diagnostics, or enzymes for chemical reactions. An international research team has now developed a method for designing large new proteins better than before and producing them with the desired properties in the laboratory. Their approach involves a new way of using the capabilities of the AI-based software Alphafold2, for which the Nobel Prize in Chemistry was awarded in 2024. Whether as building blocks, transport systems, enzymes, or antibodies, proteins play a vital role in our bodies. Researchers are, therefore, trying to recreate them or to design so-called de novo proteins that do not occur in nature. Such artificial proteins are designed to bind to certain viruses or transport drugs, for example. Scientists are increasingly using machine learning to design them." #ai #artificialproteins

This is a great discussion with Professor Samuel Kojo Kwofie. He is the Head of the Department of Biomedical Engineering Sciences in the School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana. AI in Healthcare and Poverty Alleviation https://lnkd.in/dvrCMxUA AI accelerates the drug discovery process by identifying potential drug candidates and predicting their efficacy. Machine learning models analyze biological data to pinpoint molecular targets and simulate interactions, significantly reducing the time and cost associated with bringing new drugs to market. AI has been instrumental in the rapid development of treatments and vaccines, exemplified by its role in the COVID-19 pandemic response. Click the Link. More to Learn https://lnkd.in/dvrCMxUA

Olga Kapustina, Polina Burmakina, Nina Gubina, Nikita Serov, Vladimir Vinogradov, User-Friendly and Industry-Integrated AI for Medicinal Chemists and Pharmaceuticals, Artificial Intelligence Chemistry, 2024, https://lnkd.in/e45EzyK7. (https://lnkd.in/ewnUSUEs) Abstract: Artificial intelligence has brought crucial changes to the whole field of natural sciences. Myriads of machine learning algorithms have been developed to facilitate the work of experimental scientists. Molecular property prediction and drug synthesis planning become routine tasks. Moreover, inverse design of compounds with tunable properties as well as on-the-fly autonomous process optimization and chemical space exploration became possible in silico. Affordable robotic platforms exist able to perform thousands of experiments every day, analyzing the results and tuning the protocols. Despite this, most of these developments get trapped at the stage of code or overlooked, limiting their use by experimental scientists. Meanwhile, visibility and the number of user-friendly tools and technologies available to date is too low to compensate for this fact, rendering the development of novel therapeutic compounds inefficient. In this Review, we set the goal to bridge the gap between modern technologies and experimental scientists to improve drug development efficacy. Here we survey advanced and easy-to-use technologies able to help medical chemists at every stage of their research, including those integrated in technological processes during COVID-19 pandemic motivated by the need for fast yet precise solutions. Moreover, we review how these technologies are integrated by industry and clinics to streamline drug development and production. These technologies already transform the current paradigm of scientific thinking and revolutionize not only medicinal chemistry, but the whole field of natural sciences. #drugdiscovery #alvadesc #alvascience #moleculardescriptors #cheminformatics #machinelearning #compchem

Generative AI in drug discovery: Predicting cellular responses to new compounds Accurately predicting how cells respond to new compounds is essential for advancing drug discovery. However, traditional lab-based screening of numerous compound combinations is both costly and time-intensive. Generative AI is now transforming this process. Recently introduced in Nature Communications by Qi and coauthors, a model named PRnet leverages a perturbation-conditioned generative model—a type of model trained to generate predictions conditioned on specific perturbations, such as the introduction of a new compound. This allows PRnet to predict transcriptional responses to novel chemical compounds across various cell types and disease pathways. PRnet utilizes an advanced encoder-decoder architecture with three primary components: the Perturb-Adapter, Perturb-Encoder, and Perturb-Decoder. By encoding chemical structures as SMILES strings and integrating cellular transcription data, PRnet can infer gene-level responses without requiring prior lab data on each compound. The Perturb-Adapter transforms chemical inputs, enabling the model to make predictions even for entirely new compounds. Meanwhile, the model’s latent space captures gene-level changes in transcription and adapts to different cellular environments. This design allows PRnet to perform in-silico compound screening, creating a "perturbation atlas" that maps predicted cellular responses across 88 cell lines, 52 tissues, and tens of thousands of compounds. Paper: https://lnkd.in/dytvGuNW Preprint: https://lnkd.in/dxKAtFsf #GenerativeAI #DrugDiscovery #ArtificialIntelligence #MachineLearning #Bioinformatics #PharmaceuticalResearch #HealthcareInnovation #LifeSciences #Biotech #MedicalResearch #AIinHealthcare #DeepLearning #ComputationalBiology #Biotechnology #PredictiveAnalytics

‼️ It's exciting to learn how Cell-Free Protein Synthesis #CFPS could contribute to such a breakthrough in Protein-Ligand Design with AI ‼️ 🧪 A cell-free reporter system was utilised to test a 𝘀𝗰𝗙𝗩 𝗯𝗶𝗻𝗱𝗶𝗻𝗴 with a 𝗵𝗲𝘁𝗲𝗿𝗼𝗱𝗶𝗺𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗽𝗼𝗿𝘁𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺. ✅ Read more in the article how a scFV fused to a zinc-finger transcription factor enabled the in vitro translation of a reporter protein. #CellFree #SynBio

Exciting advancements in computational techniques are revolutionising drug discovery, reducing time and costs. The powerful combination of machine learning algorithms and GPUs is driving unparalleled progress in drug development. From pinpointing targets to optimising drug candidates, computational methods are reshaping the entire discovery process. Machine learning is now predicting drug metabolism with remarkable accuracy, saving valuable time and resources. This ensures more effective and safer treatments. Breakthroughs in protein folding, like DeepMind’s AlphaFold, are decoding complex structures and paving the way for ground breaking therapies. Discover how these in silico upgrades are accelerating drug discovery. This is a fascinating long read from Anna Demming featuring insights from OVHcloud Kuano Boehringer Ingelheim Richard Bungay Google DeepMind Marc van der Kamp University of Bristol Georg Schusteritsch #quantum #AI #enzyme #machinelearning Full article in comments below 👇