BenevolentAI: Uniting Human & AI in Service Of The World

A deep tech company using advanced AI to accelerate drug discovery and solve global health challenges by combining human insight with machine intelligence.

Deep-tech represents a new wave of transformative innovation that drives both economic growth and societal progress. Unlike "shallow-tech" solutions—such as conventional app development or digital platforms that mainly disrupted traditional industries over the past decades—deep-tech is rooted in groundbreaking scientific discoveries and engineering advances. These technologies are complex, high-value, and difficult to replicate, often pushing the boundaries of what's technically possible. Their potential lies not just in commercial disruption, but in creating lasting socio-economic impact through fundamental innovation. Here in this blog, we will dive into the latest innovation in the deep-tech and its applications in deciphering and iron out complex real world problems. One among them is drug development. Drug development is a deep-tech domain because it requires breakthroughs in biology, chemistry, and data science to create novel treatments that can save or significantly improve lives. The process is long (often 10+ years), expensive (costing billions), and highly regulated. It involves understanding disease mechanisms at a molecular level, designing and testing new compounds, running extensive clinical trials, and ensuring safety and efficacy before approval.

BenevolentAI is among the pioneering ventures to address this complicated problem by conducting advanced research and unwavering innovation. Established in 2013 by entrepreneur Ken Mulvany, BenevolentAI is a London-headquartered deep-tech firm that's transforming the pharma industry by leveraging artificial intelligence. BenevolentAI is at the leading edge of deep-tech drug discovery by utilizing state-of-the-art AI and biomedical science to de-risk and accelerate novel therapeutic development—one of the most capital- and risk-intensive healthcare challenges.

We will see how BenevolentAI masterfully deploys deep-tech methods—including AI-powered knowledge graphs, machine learning models, machine reasoning and biomedical data integration—in addressing the intricately complex challenges in drug discovery.

Challenges using Traditional Drug Discovery Techniques

• High Cost: It costs more than $2–3 billion to develop a single new drug, considering research, development, and setbacks.

• High Failure Rate: More than 90% of drug candidates fail in clinical trials because of safety issues, inefficacy, or unexpected side effects.

• Long Timelines: The journey usually takes 10–15 years from discovery to approval for the market, keeping essential treatments from patients waiting.

• Low Efficiency in Multifactorial Diseases: Conventional methods are unable to cope with diseases caused by multifactorial processes such as Alzheimer's, cancer, and autoimmune diseases.

• Inefficient Use of Data: Biomedical information is extensive but fragmented and underused. Conventional methods tend to miss concealed patterns in genetics, biology, and patient responses.

• Redundancy and Trial-and-Error: Drug discovery is often a trial-and-error activity, resulting in inefficiency and redundancy.

BenevolentAI addresses and mitigates these challenges by effectively leveraging advanced deep-tech techniques to streamline and enhance the drug discovery process by uniting human and AI.

How does BenevolentAI leverage AI and deep-tech in Drug Discovery

How Artificial intelligence Meets Biology

At BenevolentAI, biology and AI are combined at the core of their strategy. The biological knowledge of disease—cellular pathways, protein-protein interactions, and gene mutations—is converted into organized data that can be reasoned upon by AI systems. By encapsulating biological knowledge into machine-readable forms, BenevolentAI enables algorithms to simulate biological complexity and make inferences that would be difficult or impossible for human researchers to detect alone. This interaction enables scientists to find links that can result in completely new treatments for diseases for which there are no cures.

Using Artificial intelligence and Machine Learning

BenevolentAI leverages cutting-edge artificial intelligence (AI) and machine learning (ML) methods across the entire drug discovery pipeline to identify new therapeutic targets and create better treatments in less time. Their platform consumes and interprets enormous amounts of biomedical data—ranging from scientific literature, clinical trials, genetic databases, and patient records—to discover latent connections between genes, diseases, drugs, and biological pathways. Machine learning models learn to propose hypotheses regarding mechanisms of disease, anticipate possible drug-target interactions, and optimize selection of compounds. It decreases trial-and-error reliance and greatly minimizes the time and cost associated with conventional methods of discovery.

The Knowledge Graph and Machine Reasoning

Better decision depends on the foundation of information across various discipline fundamentals like biology, chemistry, genetics, patients information they integrate all this data into their proprietary information generated by their AI models. Unique insights are extracted from existing data and experimental results from their in-house labs. The goal is to holistically represent biomedical knowledge and ultimately enhance their understanding of human biology.

A dynamic, structured representation of millions of biological and medical entities and their interrelationships. Nodes in the graph represent entities such as genes, proteins, diseases, and drugs, while edges represent the relationships between them (e.g., inhibition, activation, association).

This is how BenevolentAI identified a potential treatment for COVID-19 early in the pandemic by proposing baricitinib as a candidate—later validated and approved for emergency use.

Major Challenge: Training over Sparse, Discrete, Noisy, Complex, Enormous Amount of Dataset

Natural Language Processing (NLP) plays a crucial role in BenevolentAI’s platform by extracting biomedical knowledge from unstructured text, which comprises the majority of scientific information. Scientific literature, patents, and clinical trial data are vast, sparse, noisy, and written in highly technical language.

BenevolentAI’s NLP models are trained specifically on biomedical corpora to recognize and extract meaningful entities (e.g., drug names, gene mutations, symptoms) and their relationships from this complex text. Advanced NLP techniques like named entity recognition (NER), dependency parsing, and semantic role labeling allow the system to structure this data and feed it into the knowledge graph. This helps the AI continuously learn and reason from the ever-growing body of biomedical research.

Impact They Created

Discovered a COVID-19 Treatment in Record Time: BenevolentAI's platform identified Baricitinib, an existing rheumatoid arthritis drug, as a potential COVID-19 treatment within just days at the start of the pandemic.

Accelerated Rare Disease Research: BenevolentAI has targeted rare neurodegenerative diseases (progressive neurological disorders characterized by the gradual loss of nerve cells (neurons) in the brain or peripheral nervous system), where traditional pharmaceutical research has often failed due to complexity and cost.

Reduced Drug Discovery Time and Cost: Traditional target identification takes 4–6 years. BenevolentAI’s platform can do it in months along with Fewer failed drugs, lower R&D cost, faster path to treatment.

Takeaway

BenevolentAI shows us what deep tech really means. It's not just about building with AI—it's about applying first-principles thinking, scientific knowledge, and advanced engineering to solve problems that actually matter. Problems that affect lives. Problems that the world has struggled with for decades.

This is the kind of innovation that doesn’t chase trends—it creates breakthroughs.

Solving real problems with deep tech isn’t optional anymore— because it matters. Yeah, it does!!