How AI Is Changing Science: Top 10 Research Tools to Use in 2025
- Professor Alexander Solovev
- Jun 4, 2024
- 9 min read
Updated: Jun 12
Artificial intelligence (AI) has fundamentally reshaped the practice of scientific research by 2025. Advanced AI systems now automate complex tasks that once required months of manual work, dramatically reducing the time and effort needed for scientific discovery [1]. From drug design to molecular simulations, AI is accelerating workflows, increasing precision, and lowering costs across disciplines [2]. This review highlights ten groundbreaking AI tools that are redefining how modern science is conducted, offering researchers new levels of efficiency and insight (Fig. 1).

Figure 1. Researcher is using new AI Tools to accelerate scientific discoveries.
1. AlphaFold 3 – Protein Structure Prediction and Molecular Interactions
AlphaFold 3 marks a pivotal advancement in the field of computational biology, offering a significant improvement in accuracy over previous biomolecular modeling platforms [3]. At the core of this innovation is a diffusion-based neural network architecture capable of co-predicting the structures of complex biomolecular assemblies—including proteins, nucleic acids, small molecules, ions, and chemically modified residues—with remarkable precision [3].
This capability has opened new avenues in structural biology, enabling atomically accurate predictions that are reshaping early-stage drug discovery workflows [4]. The influence of AlphaFold 3 extends well beyond protein folding. Its superior performance in modeling protein-ligand and protein–nucleic acid interactions now surpasses the accuracy of conventional molecular docking tools [3].
Additionally, it offers notable improvements in predicting antibody–antigen interactions, a critical area for immunotherapeutic development [3]. Nevertheless, despite its advances, the model currently replicates only 43% of experimentally observed chalcogen bonds in its predictions, underscoring the importance of ongoing experimental validation to complement AI-based simulations [5].
2. DORA AI Scientist – Multi-Agent Research Assistant
DORA (Draft Outline Research Assistant) introduces a transformative approach to automated scientific research through the deployment of hierarchical multi-agent systems composed of both general-purpose and domain-specific large language models (LLMs) [6].
This innovative platform supports a wide range of research functions by orchestrating multiple AI agents that collaboratively perform specialized tasks—from hypothesis generation to structured report composition [6]. Designed to streamline the research pipeline, DORA provides configurable templates and workflow modules for the semi-automated execution of key stages in the scientific process. These include literature reviews, experimental design, data analysis, and the drafting of scholarly manuscripts [6].
By integrating with open-access data repositories and internal knowledge platforms, DORA enhances its analytical depth, particularly in high-throughput biomedical research and early-phase target discovery [7]. The platform is continuously updated based on user engagement and emerging research needs, fostering a dynamic environment where human-AI collaboration advances rapidly. As a result, DORA is increasingly recognized as a valuable tool for accelerating research productivity and ensuring high-quality, citation-verified scientific outputs [6].
3. Elicit – AI-Powered Literature Review and Research Assistant
Elicit represents a significant advancement in AI-assisted literature discovery, leveraging advanced language models to automate critical elements of the research workflow [8][9]. The platform is engineered to parse and analyze large volumes of scientific publications at high speed, efficiently summarizing articles, extracting relevant data, and identifying key findings with minimal user input [8].
By assisting in the rapid identification and synthesis of relevant literature, Elicit helps researchers locate, organize, and evaluate multiple studies on a given topic with notable efficiency. According to usage data reported by its developers, the platform saves researchers an average of 1.4 hours per week by optimizing the literature review process [9]. Its structured, table-based interface enables users to view summaries of top-cited papers and assess the strength of evidence across different sources [8].
In addition to its summarization capabilities, Elicit supports early-stage research ideation. The platform facilitates hypothesis development by suggesting search terms, highlighting research gaps, and enabling question-driven exploration [9]. These features make it an indispensable tool for researchers seeking to conduct efficient, comprehensive, and well-structured reviews of existing scientific knowledge.
4. BenevolentAI – AI-Augmented Drug Discovery Platform
BenevolentAI has emerged as a leading innovator in AI-driven drug discovery, with a strategic focus on improving therapeutic success rates through the early identification of viable drug targets [10]. Its integrated platform operates across three primary pillars: the development of proprietary pipelines targeting high unmet medical needs, collaborative discovery partnerships with major pharmaceutical companies such as AstraZeneca and Merck KGaA, and the provision of Software as a Service (SaaS) tools that enable knowledge exploration across biomedical datasets [10].
By applying machine learning algorithms to diverse biomedical data sources, the platform facilitates the discovery of complex relationships and novel hypotheses that guide target identification and compound development [2]. A notable example of its translational impact is the rapid repurposing of baricitinib—an existing Eli Lilly drug—for the treatment of COVID-19, based on mechanistic insights generated through BenevolentAI’s platform [10].
This multifaceted model combines scientific innovation with commercial scalability. Revenue is generated through internal pipeline advancement, milestone-driven industry collaborations, and licensing of AI-powered SaaS tools designed to empower data-driven research [10]. BenevolentAI thus exemplifies the application of artificial intelligence to accelerate drug development pipelines while reducing the risks associated with early-stage therapeutic discovery.
5. NVIDIA Modulus – Physics-Informed AI Simulation Platform
NVIDIA Modulus, previously known as PhysicsNeMo, introduces a paradigm-shifting approach to scientific simulation by integrating artificial intelligence with the governing laws of physics [11][12]. The platform leverages physics-informed neural networks (PINNs) and partial differential equations (PDEs) to construct high-fidelity, parameterized surrogate models. These models enable rapid and accurate simulations across a wide range of domains, including computational fluid dynamics, structural mechanics, heat transfer, and electromagnetics [11].
Modulus is particularly well-suited for solving coupled systems and complex physical phenomena where conventional solvers are limited by computational cost or the need for labeled data. It supports forward modeling without training data, inverse problem-solving, and real-time data assimilation, making it a versatile tool for both academic research and industrial design applications [13].
A defining advantage of Modulus lies in its ability to simulate entire parameter spaces in parallel, drastically reducing computation times. For example, in a recent collaboration with Ansys, the integration of Modulus yielded more than a 100-fold acceleration in thermal simulation runtimes compared to traditional finite-element methods [14]. This efficiency enables rapid prototyping and optimization in settings where simulation speed and accuracy are critical.
6. Consensus – AI-Powered Academic Search Engine
Consensus represents a major advancement in academic information retrieval, offering an AI-powered search engine built upon the Semantic Scholar corpus, which comprises over 200 million peer-reviewed publications [15]. Unlike conventional search engines that return lists of hyperlinks, Consensus is designed to deliver concise, evidence-based summaries in response to user queries. It extracts and synthesizes key findings directly from academic literature, providing researchers with immediate insight into supporting or opposing evidence on a given topic [15].
The platform is optimized for precise scientific inquiry, particularly excelling in searches involving direct research questions, concept pairings (e.g., “intermittent fasting and blood glucose”), and exploratory queries [15]. By presenting literature in the form of knowledge snapshots rather than definitive conclusions, Consensus encourages critical evaluation and facilitates nuanced understanding of evolving scientific debates.
Additionally, when full-text access is available, the platform enables users to delve deeper into primary sources, ensuring transparency and traceability in evidence-based research [15]. This approach enhances both the efficiency and depth of literature exploration, making Consensus an increasingly valuable tool for academics, students, and research professionals.
7. Ginkgo Bioworks – Protein Large Language Model and API
Ginkgo Bioworks has introduced a pioneering suite of protein-focused large language models (LLMs), designed to accelerate advancements in drug discovery and synthetic biology [16][17]. These models are built on Google Cloud’s Vertex AI platform and trained using Ginkgo’s extensive proprietary datasets, enabling researchers to analyze, interpret, and apply protein and DNA sequence data with unprecedented efficiency [17].
The platform’s initial release, known as ginkgo-AA-0-650m, incorporates over 2 billion proprietary protein sequences, positioning it among the most extensive LLM-based biological tools currently available [17]. Through its API, users gain access to advanced capabilities for sequence analysis, function prediction, and protein engineering, offering substantial value to pharmaceutical, biotechnology, and life sciences companies [16].
Ginkgo’s business model emphasizes accessibility and data privacy. The service operates with low-cost token pricing, no royalty obligations, and a firm policy against reusing customer data. This framework supports iterative, in silico protein design workflows that are both scalable and secure, allowing researchers to conduct high-throughput experimentation without compromising intellectual property [16].
8. Research Rabbit – AI-Enhanced Literature Discovery and Visualization
Research Rabbit has introduced a novel paradigm in academic literature discovery by combining artificial intelligence with dynamic visualization tools that map scholarly relationships [18]. Originally launched in 2021, the platform allows users to explore connections among research articles, authors, and topics by generating interactive citation maps and curated publication trails from a selected “seed” paper [18].
The system’s underlying algorithms leverage citation networks and custom heuristics that extend beyond traditional bibliometric analysis, offering intelligent recommendations based on both citation lineage and thematic relevance [18]. This capability is particularly valuable for navigating unstructured research queries, enabling users to discover earlier, later, and thematically similar works with minimal manual input.
Research Rabbit also facilitates the identification of academic clusters and collaborative networks, helping researchers uncover key contributors and evolving fields of inquiry. Its intuitive interface and seamless integration with reference managers make it especially useful for synthesizing literature reviews and constructing research proposals. Notably, the platform claims to host the second-largest academic article database after Google Scholar, underscoring its scale and relevance for contemporary scientific exploration [18].
9. IBM Watson Discovery Advisor – Cognitive Research Analytics
IBM Watson Discovery Advisor stands at the forefront of cognitive computing applications in scientific research, offering a powerful platform designed to dramatically scale and accelerate the research process [19]. By leveraging Watson’s advanced natural language processing capabilities, the system enables research teams to reduce the time required for hypothesis testing and knowledge synthesis from months to days—or even hours—transforming the pace of innovation in R&D environments [19].
The platform is particularly well-suited for the life sciences, where its capacity to interpret the nuanced language of scientific texts—such as chemical compound interactions and biomedical mechanisms—provides significant analytical depth [19]. Watson addresses one of the most pressing challenges in modern science: the overwhelming volume of new publications, with an estimated paper emerging every 30 seconds. It assists researchers in navigating this vast information landscape by identifying latent connections, suggesting novel hypotheses, and uncovering non-obvious patterns across disparate datasets [19].
Through its ability to unify and analyze diverse sources of structured and unstructured data, Watson Discovery Advisor offers a transformative approach to scientific discovery—empowering researchers to make data-driven insights at scale.
10. Scholarcy – AI-Powered Academic Summarization and Analysis
Scholarcy has emerged as a valuable AI-driven tool for academic research, offering automated summarization and content structuring functionalities that streamline the analysis of scholarly texts [20]. Designed with both students and researchers in mind, the platform transforms academic articles, book chapters, and technical documents into interactive flashcards that highlight core arguments, methodologies, and findings [20].
In addition to its summarization engine, Scholarcy supports efficient literature management through tools that enable annotation, note-taking, and export of structured summaries in a variety of formats. These features are particularly useful for constructing annotated bibliographies and preparing systematic reviews [20][21].
Scholarcy's algorithms are capable of parsing extensive academic datasets, allowing users to rapidly identify thematically relevant publications, uncover research gaps, and enhance argumentation through evidence-based synthesis [22]. By generating visual concept maps and digestible content summaries, the platform facilitates deeper understanding of complex scientific material and significantly reduces the time required for comprehensive literature reviews [21].
Impact and Future Directions
Collectively, the AI tools discussed in this review signify a paradigm shift in the conduct of scientific research, marking a departure from traditional manual methodologies toward AI-augmented workflows that enhance both efficiency and analytical precision [1][23]. The integration of artificial intelligence across the entire research continuum—from hypothesis generation and literature synthesis to data interpretation and scholarly publication—is enabling researchers to address increasingly complex scientific challenges with greater speed and sophistication [24].
The convergence of these AI technologies points toward a future in which intelligent systems function as indispensable collaborators in the research process. These systems can analyze vast and heterogeneous datasets, detect latent patterns, and generate testable hypotheses that may be beyond the reach of conventional human cognition [25][23]. As these tools continue to evolve and become interoperable with other emerging technologies, they hold the potential to open new avenues for scientific discovery and to democratize access to high-impact research infrastructure across diverse institutional and geographic contexts [24][22].
However, the accelerated adoption of AI in research also brings forth critical considerations regarding transparency, reproducibility, and the responsible disclosure of AI contributions in scientific publications [26]. To safeguard research integrity and public trust, the scientific community must proactively establish guidelines and ethical standards for the use of AI in scholarly work. Doing so will be essential for harnessing the full transformative potential of these technologies while upholding the rigor and reliability that underpin the scientific enterprise [26][23].
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