Latest news with #ProteinDataBank
Time of India
21-04-2025
- Business
- Time of India
Integrating AI into business operations for real-world impact
Embedding AI into business operations isn't about plugging in a tool—it's about transforming how decisions are made. Drawing parallels from scientific breakthroughs like AlphaFold, this article explores the five foundational elements that empower organizations to evolve into AI-native enterprises capable of intelligent, real-time action. From Data to Decisions In 2020, DeepMind stunned the scientific community with AlphaFold 2, solving the decades-old challenge of protein folding. This breakthrough wasn't merely about deep learning or compute power. It was made possible by a confluence of factors: vast public datasets, a clearly defined challenge, collaborative ecosystems like the Critical Assessment of Structure Prediction (CASP), and the foundational Protein Data Bank. This offers a compelling parallel for enterprises. Just as AlphaFold's success stemmed from shared frameworks, open collaboration, and a clear purpose, AI breakthroughs in business demand more than algorithms—they require foundational readiness across data, governance, systems, and teams. From Predictive to Prescriptive: The AI Advantage Predictive analytics laid the groundwork for data-driven strategies . But as data volumes explode to the tune of exceeding 180 zettabytes by 2025, organizations must evolve beyond prediction—toward systems that adapt, recommend, and act in real time. In manufacturing, for instance, AI goes beyond forecasting equipment failures—helping dynamically reschedule maintenance, triggering automated ordering of spare parts, and minimizing downtime. In retail, while predictive analytics help determine trends and assess customer behavior, AI enables intelligent pricing, localized inventory decisions, and hyper personalization. Building Blocks for AI Integration Data Readiness: From Silos to Systems AI is only as good as the data it learns from. Yet many enterprises operate in fragmented data environments. Transitioning to AI-ready platforms is essential—combining data lakehouses, real-time pipelines, and governance tools to democratize access and enable actionable intelligence. These architectures support multi-modal data, decouple storage and compute, and enable intelligent workflows across functions. Cross-functional Alignment and Talent Strategy Embedding AI isn't solely a technology initiative. It requires a well-coordinated effort between data scientists, domain experts, and process owners to convert models into meaningful outcomes. Organizations with centralized AI governance and strong executive sponsorship are more likely to scale AI successfully. Human-AI collaboration becomes essential, especially in high-stakes decisions where oversight, ethics, and context matter. Responsible Governance and Explainability With AI systems playing a key role in the core operations, trust and transparency become non-negotiable. AI systems should be auditable, explainable, and aligned with regulatory frameworks. When it comes to AI governance, data privacy is a critical element to consider, for it helps protect IP while ensuring compliance. Effective governance will not only help strengthen security but also build enterprise resilience. AI Architectures and Embedded Intelligence AI is shifting from being a bolt-on analytics layer to becoming an integral part of enterprise workflows. Modern enterprise platforms treat AI not as an add-on, but as foundational logic—driving decisions inside ERP, CRM, and SCM systems. Tech giants like Microsoft (Copilot), Salesforce (Einstein), and SAP (BTP), have set a benchmark for embedded intelligence. Intellectual Property and Competitive Edge As enterprises build and train proprietary models—especially domain-specific or task-specific models—they are creating strategic IP. This intellectual capital must be protected—via disciplined model lineage, data provenance, and secure deployment frameworks. It also opens doors for monetizing AI through platforms, APIs, and services that extend beyond internal efficiency to ecosystem leadership. Embedded AI in Action: Industry Snapshots HealthcareAutomotiveFinancial ServicesRetailAI helps optimize patient flow by reallocating resources dynamically across emergency rooms, labs, and diagnostics. Innovators like MDI, Activ Surgical, and Cala Health are embedding AI into surgical robotics, disease detection, and digital connected cars generating terabytes of data per hour, OEMs are embedding edge AI for local processing—powering safety features, predictive maintenance, and new business models like are moving beyond customer experience and fraud detection—embedding AI in real-time credit scoring, risk management, and personalized wealth management. Even regulators like RBI are leveraging AI to derive insights from supervised is powering micro-fulfilment centers, optimizing last-mile delivery, and enabling hyperlocal demand prediction. Walmart reportedly reduced stockouts by 30% using AI. Indian players like Flipkart, Myntra, and Blinkit are leveraging AI to redefine customer experience and last-mile delivery. The AI-Native Enterprise: A Strategic Imperative AI is not the end goal—it's a force multiplier. Enterprises that make AI integral to their DNA benefit from: Innovation & Agility with faster R&D cycles, scenario simulation, and market-fit experimentation Real-time Decisioning enabled by Intelligent automation and dynamic optimization across functionsCost Optimization through predictive maintenance and resource allocationRevenue Growth via hyper-personalization and next-best action recommendations and AI-native services AI success in the enterprise hinges on foundational readiness. Companies that embrace data as an asset, embed AI into operations, build responsible governance, and foster cross-disciplinary collaboration will thrive, becoming AI-native enterprises.
The Guardian
28-03-2025
- Science
- The Guardian
AI may help us cure countless diseases – and usher in a new golden age of medicine
AlphaFold might be the most exciting scientific innovation of this century. From Google DeepMind, and first reported in 2020, it uses artificial intelligence to figure out a protein's 3D structure. The technology has already been used to solve fundamental questions in biology, awarded the Nobel prize (in chemistry – to Demis Hassabis and John Jumper) and revolutionised drug discovery. Like most AI, it's only getting better – and just getting started. A protein's structure gives us clues about its function, and helps us design new drugs. AlphaFold, which was trained on a huge database of experimentally solved structures called the Protein Data Bank, predicts a protein's structure based on its amino acid sequence. In the past, the first step would be to produce a vast amount of protein – using litres of a bacterium, or a virus. You'd pray for the protein to assemble into a crystal lattice (notoriously difficult), and then fire high-energy X-rays at it. This is called X-ray crystallography, and it could take years. Now, AlphaFold can do it in minutes (and a hell of a lot more cheaply, too). When AlphaFold competed at a protein structure-solving competition in 2020, it was so good that some accused the AlphaFold team of cheating. At its first appearance, AlphaFold became the state of the art. Now, there are approximately 250,000,000 protein structures in the AlphaFold database, which has been used by almost 2 million people from 190 countries – many more people than can do X-ray crystallography! I did my PhD on cancer biology. I would have loved to solve the structure of the protein I worked on. Maybe I could have even used it to make a new drug. Now, I can go to the AlphaFold server and produce a structure in five minutes that would have consumed my whole PhD. Dr Pauline Lascaux, a molecular and structural biologist at the University of Oxford, said that AlphaFold was central to her latest study, which discovered a new way that cells repair DNA, and that more than 90% of the studies she reviews are citing it. So what does drug discovery with AlphaFold look like? In this recent Science study, researchers used AlphaFold to predict the structure of the serotonin receptor, which controls mood. Through in silico testing (on computers) they tested which of 1.6bn (!) molecules could bind the AlphaFold structure. What they found was a series of molecules that bound much more tightly than drugs generated via the conventional – experimental – approach, which could be new drugs for mood disorders. AlphaFold has only been around since 2020, but its impact has been meteoric. Here are the top three discoveries enabled by AlphaFold so far: Solving a decades-old problem: the structure of the nuclear pore complex, one of the biggest structures in the cell. This complex is the guardian of entry to the nucleus, which holds the cell's DNA. It's implicated in cancer, ageing and neurodegeneration – and now we know what it looks like at the atomic level. Finding a new liver cancer drug. In a lab (not in patients), the drug, which targets the cancer protein CDK20, prevented liver cancer growth. Helping to design a 'molecular syringe', which delivers a therapeutic protein payload into human cells. There are companies built on AlphaFold, too. If AlphaFold is solving the lock, then AlphaProteo provides the key. AlphaProteo uses AlphaFold's structures to design molecules that can bind to and modulate other proteins. This has been used to generate molecules that have never been made before – to target Covid-19, cancer and autoimmunity. Also from DeepMind, AlphaMissense tackles the problem of missense mutations – minor changes to genes, with uncertain functional impact. Despite their prevalence, we only know whether about 2% of these changes are pathogenic. AlphaMissense models the structure of the mutations using AlphaFold: if the protein structure changes, it's probably pathogenic. This could transform the diagnosis and treatment of rare genetic diseases. We don't know yet whether drugs designed using AlphaFold will pass successfully through clinical trials, and none have been tested in humans yet – only time will tell. In the future, AlphaFold could enable new medicines to be discovered by individuals, could find drugs for undruggable targets, and could unlock the secrets of molecular life. (Just the other day, AlphaFold helped to solve the structure of the sperm-egg bridge that forms during fertilisation.) If the first generation of drug discovery was the nature generation, which gave us aspirin (from willow tree bark), and the second was the biotech generation, which gave us Ozempic, then we've now moved to the third generation: the AI generation. Samuel Hume is a fellow at The Foulkes Foundation and pursuing PhD in the University of Oxford's department of oncology
