New Delhi, Jan. 9 -- Large volumes of complex data in research and development labs offer vast potential for scientists in India. Yet the race to finish first by extracting insight is often slowed down by manual data analysis. Indian labs are now turning to AI-enriched solutions, which help streamline research workflows and significantly accelerate established development processes.
As investment in research and development (R&D) across many sectors grows, India has emerged as aglobalpowerhouse.For example, according to the Indian Ministry of Science and Technology,the countryhas experienced a 2.5x growth in scientific publication output over 10 years,a rate that shows no signs ofslowingdown.
The growth inthe number ofpublicationsis driven by increased research activity,which in turn isgeneratingasignificantboostin the volume and complexity of data.In addition, asglobal research advances, the range of data that laboratories must handle continues toexpand -from structures to composition for small molecules and the newest large-molecule modalities.Atthe same time, open-science principles encourage researchers worldwide to share and collaborate, adding to the range and complexity of the data landscape.
Even as the flood of data continues to increase, many labs continue to rely on manual work to unite and analyze data across isolated spreadsheets, databases, and notebooks. For such teams, the insights that help to drive the "decide" step of the "design-make-test-decide" research cycle are significantly delayed as they wait for data to become available.
While manual information processes can produce rich, holistic research insights, the pace of today's work environment means that electronic tools are essential. Over the years, R&D labs have introduced a variety of systems designed to speed up data analysis, but often these are highly specialized solutions that operate in departmental silos. In addition, a tool tailored to structured data will not work for unstructured data, limiting capacity and capability.
As a further complication, although science aims to be an objective discipline, it is natural that researchers view their work through a subjective lens. Hypothesis generation and data interpretation will always be limited by innate human biases. Researchers may subconsciously favor data that confirms their pre-existing beliefs and hypotheses, particularly if counterevidence is difficult to obtain.
Working in isolated, fragmented data silosmay tend tointroduce human bias and error,asrestricted accesscan become a self-limiting factor, as moving back and forth between databases and paperwork is a slow and labor-intensive process. In some cases, data analysis becomes the most time-consuming step in a research project, taking away from precious time that could be spent on testing and discovery.
Introducing aComprehensive,AI-Powered R&DSolution
Revvity Signals transforms how scientists approach R&D by eliminating bottlenecks through AI-powered data interpretation and decision-making. Signals One, delivers a comprehensive workflow solution that makes both structured and unstructured data AI-ready, ensuring researchers extract maximum value from their datasets.
At its core, Signals One combinesRevvity'sSignalsdatamanagement capabilities,Spotfire
analyticsandgenerative AI,to enable rapid data retrieval and analysis.The solutionautomatespattern detection and clusteringacrosslarge datasets, freeing analysts fromlabor-intensivemanual processes and giving real-time insights for data-driven decision-making.Scientists canleverageguidedsemanticsearchtoquery datausing natural language, receiving sub-second responses even from massive databases,to quicklyidentifythe most promisingcandidates.
Embedded generative AI and machine learning capabilities in Signals One revolutionize hypothesis testing and design of experiments (DoE). By reducing personal bias and suggesting optimal test strategies, Signals One helps researchers minimize experimental iterations while maximizing results.For antibody development, Signals One's specialized AI tools predict structures from sequences, estimate accessible surface areas around potential liabilities, and score candidates on developability metrics. Thus, it allows scientists to focus resources on the most viable designs.
Advanced capabilities for small-molecule drug discoveryintegrate ChemDraw(R) for molecular designalongside comprehensivecompound registration and inventory management. Signals One features includematched-molecular pair analysis, chemical clustering and multi-parameteroptimization(MPO) dashboardsthatlinkcompound profiles acrosscritical attributes likepotency, selectivity, solubility, permeability and clearance attributes.This integrated approachaccelerates the journey from initial concept to high-confidence drug candidates.
Signals One's cloud-native SaaS architecture supports the complete "Design-Make-Test-Decide" lifecycle while enabling seamless multidisciplinary collaboration across chemistry, biology, and modalities. By adhering to FAIR (Findable, Accessible, Interoperable, Re-usable) data principles, the solution ensures AI-ready data maintains the highest quality standards for reliable, cross-program utility.
BoostingMission-CriticalInvestment
According to the UN,India ranks 10thin the worldfor private investment in AI, securing US$ 1.4 billionin 2023.Realizing the potential rewards on offer from AI, the government is steppingup its interest, as the Finance Minister announcedat the beginning of 2025fundingforCenters of ExcellenceforAIineducation.Together, India's private and public sectors can democratize access to AI-powered solutions, enabling the broader scientific community to make the most of cutting-edge analytics.
AI-enhanced analyticspresent an opportunity for Indian R&D labs to shoot ahead of typical modernization and digitalization initiatives.Many global competitors continue to struggle with their digital journeys, as they move from manual processes through spreadsheets and legacy software;Indian labshave the opportunity toentirely reinvent what R&D processes look like, by vaulting straight from manual processes to the new AI-driven world.With AI-supported solutions empowering more wet-lab scientists to make sense of data without relying on analysts,researcherscouldfurtherstreamlineoperations and boost collaboration.
Leveraging AI toStayAhead of theCompetition
With investment in AI solutions on the rise,R&D labs in Indiacanboost efficiency, enhance quality and accelerate development cycles like never before.While human expertise will always remain central to R&D,AI and LLMs can provide rapidsuggestionsandcorrelationsto amplifyinsight.In the rapidly changing global research environment, selecting the right technology to support decision-making is crucial.
By adopting platforms such as Signals One, researchers in India can accelerate the "design-make-test-decide" cycle, leaving behind the days of copying and pasting information across data silos and spending hours looking for hidden patterns in the data. Rich, intuitively-presented datacorrelations can be at scientists' fingertips-all while keeping IP safe within the secure Revvity Signals environment.
By integrating experimentation in the lab with AI-augmented predictive modeling, researchers can reduce repetitive lab work, quickly extract insights from vast datasets, and spend more time doing what matters most: collaborating and engaging in critical thinking to solve the world's most pressing challenges.
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Published by HT Digital Content Services with permission from PNN.