How AI in life sciences is accelerating the drug discovery lifecycle

One of the most time-consuming and expensive endeavors in science is bringing a new drug to market. For small-molecule drugs alone, it typically takes 10 to 15 years and hundreds of millions of dollars, with up to 75% of R&D investment lost to failures. In an industry where time, cost, and uncertainty define the process, AI in life sciences is quickly emerging as a differentiator.

AI is transforming how companies approach drug discovery and development. It speeds up data analysis, target identification, and compound screening.

Here are four key stages where AI in life sciences is reshaping the path from lab to launch.

1. Uncovering new starting points: AI in target identification

Every new drug begins with a crucial first step: the identification of the right biological target. Traditionally, this has involved years of painstaking research, with researchers hoping on known mechanisms and hit-or-miss approaches to find one gene or protein linked to a disease. It’s a process too often slowed down by restricted access to data and hand-cranked analysis.

AI in life sciences is revolutionizing this first stage of drug discovery. By combining machine learning with biomedical data sets, from genomics to patient-derived cell cultures, AI systems can unveil new targets much faster and more accurately. 

Platforms sift through millions of data points to predict which molecular interactions will have the greatest chance of affecting the disease outcome, even in complex neurodegenerative disease.

A recent example is BenevolentAI’s development of BEN-34712, a promising candidate for treating amyotrophic lateral sclerosis (ALS). Working in partnership with the University of Sheffield’s SITraN, BenevolentAI used its proprietary AI platform to identify a target involved in impaired retinoic acid signaling, a known contributor to ALS pathology. 

What’s notable is the speed and precision of the outcome: BEN-34712 achieved IND-enabling studies, with solid preclinical justification, in just a few years. This is compared to the decades it would have taken under traditional discovery. It’s a clear sign of how AI in life sciences is not only accelerating drug discovery but also enabling smarter, more targeted development from the start.

2. Finding the needle faster: AI in hit identification and screening

Once a target is validated, the identification of “hits”—chemical compounds that can effectively interact with the target is the next critical phase of the drug discovery process. 

Traditional high-throughput screening (HTS) involves screening millions of molecules through laboratory-based assays, but it’s time and resource-intensive and is frequently plagued by false positives. Virtual screening (VS) based on computer simulation of the interactions offers an alternative, but even this runs for weeks for screening through billion-compound libraries.

AI in life sciences is transforming both methodologies, not just by rendering them faster, but smarter. In HTS, AI algorithms filter noisy data sets, flawlessly distinguishing between actual biological response and assay artifacts. In VS, deep learning accelerates binding affinity predictions at scale, processing complex molecular structures in hours, rather than days.

One of these breakthroughs is the use of Minimal Variance Sampling Analysis (MVS-A)—a machine learning strategy that applies gradient boosting models directly to raw HTS data. Instead of relying on pre-set interference rules, MVS-A tracks how the model learns over time to find compounds with genuine biological activity and remove confounding results. This enables researchers to prioritize the best potential hits from the outset, without going down costly dead ends.

Real world example

A great example of smart screening in practice is illustrated by MIT’s Antibiotics-AI Project. Researchers employed deep learning to screen more than 12 million commercially available compounds and identified a new class of antibiotics that are able to kill methicillin-resistant Staphylococcus aureus (MRSA). This is a lethal superbug that kills more than 10,000 people in the U.S. each year.

What distinguishes this method is not only the pace and volume, but also its accuracy. The researchers used 39,000 known compounds to train models and then went further to exclude any candidates that showed possible human cell toxicity. This resulted in two very promising molecules that lowered MRSA infections by a factor of 10 in mice with very little harm done to human cells. This represents a huge step forward in the efficiency and explainability of AI in drug discovery.

All of these advances highlight the ways in which AI in life sciences is changing hit discovery—not only by accelerating it but by enabling more accurate, data-driven decisions that bring higher-quality candidates into the pipeline.

3. Designing better molecules: AI in lead optimization

Getting a hit is only the beginning. The second phase, lead optimization—is to turn that initial hit into a drug candidate. That means optimizing potency and selectivity, making the molecule bioavailable and bioactive in the body, and lowering toxicity. 

Traditionally, chemists cycle through tweaking the molecule structure based on Structure-Activity Relationships (SAR), varying one property at a time. It’s a time- and manpower-intensive process that can take years.

AI in life sciences is accelerating and transforming this phase through generative design. AI models are now able to propose new molecules built from the ground up, and these molecules are optimized for multiple pharmacological and biophysical criteria simultaneously. Instead of reacting to experimental data, researchers can create better leads with higher chances of success—before they enter the lab.

Real world example

A notable example is the 2022 collaboration between Peptilogics and Orion Biotechnology. The collaboration is aimed at applying AI-driven design to a previously undruggable G protein-coupled receptor (GPCR)—a target family at the center of many life-threatening diseases. 

Peptilogics’ Nautilus™ AI platform uses deep generative models, predictive simulations, and a supercomputing architecture to generate optimized peptide therapeutics. Coupled with Orion’s proprietary multiplex synthesis and receptor-ligand shape mapping platform, the collaboration is breaking new ground in AI-driven, multiparameter lead optimization.

This approach allows the teams to probe vast chemical and structural spaces, rapidly identifying high-quality candidates. In contrast to traditional screening, the coupled AI-biophysics approach is optimized to generate drug-like peptides with improved potency, tailored signaling, and synthesizability—driven by real-time feedback loops between machine learning and biological data.

This collaboration illustrates the way life sciences AI in life sciences is reshaping the lead optimization process: not just accelerating it, but also making it more predictive, focused, and capable of tackling challenges once thought to be out of reach.

4. Predicting success earlier: AI in preclinical assessment

Before being tested in human beings, a candidate drug must undergo intense preclinical screening in the guise of in vitro (laboratory) and in vivo (animal) tests. These check major parameters like safety, efficacy, and pharmacokinetics—drug absorption, distribution, metabolism, and excretion. 

But success in animal models is no sure thing with a majority of such candidates flopping in human tests due to unforeseen toxicity or poor ADMET profiles. Such failures at the end stages are not only expensive but also deny possibly promising medicines access to patients.

This is where AI in life sciences is becoming a force multiplier.

AI learns from large datasets, including past preclinical results, molecular structures, and biological interactions. It predicts ADMET properties faster and more accurately than traditional computational methods.

AI also decodes complex experimental data to simulate how a drug might behave in the human body—long before clinical trials begin.

Real world example

VeriSIM Life’s BIOiSIM® platform, which uses hybrid AI and mechanistic modeling to simulate drug behavior across biological systems.

Its Translational Index™ provides an early predictive score based on a compound’s structure, target, and pathway. This score helps indicate whether the candidate is likely to succeed in clinical trials. In a schizophrenia case study, BIOiSIM analyzed 20 previously tested drug candidates and accurately forecast their clinical outcomes. Of the candidates that scored above the platform’s threshold, 93% later received FDA approval.

By enabling early risk measurement, tools like BIOiSIM help scientists prioritize candidates with a higher probability of clinical success. They also reduce reliance on large-scale animal testing. And allow researchers to design studies more efficiently by focusing resources where they matter most.

As AI in life sciences advances, its role in de-risking preclinical pipelines is no longer optional. It’s becoming essential for faster, smarter, and more ethical drug development.

This isn’t just about improving efficiency. AI is fundamentally changing how decisions are made in early-stage drug discovery.

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The road ahead: An AI-accelerated future

Drug discovery remains a challenging process. But AI in life sciences is clearly paving the way for a more efficient future.

AI helps detect new targets within complex biological data. It also supports the design of custom molecules and predicts how they will perform in preclinical studies.

By addressing key bottlenecks, AI is streamlining some of the most critical stages in drug development.

Though issues relating to data quality, model explainability, and verification persist, the momentum is inescapable. With advancing generative models and multimodal data integration, AI in life sciences will go on to play an increasingly important role in speeding up innovation and R&D. The future of AI development holds much promise for a fundamental change in the way we design new medicines.

At Netscribes, we empower healthcare and life sciences companies to lead the way. With customized AI solutions, spanning from AI advisory and ML Ops to workflow automation. 

Our offerings assist in process streamlining, model reliability, and extracting actionable insights from disparate data. Learn about our healthcare and life sciences solutions to discover how we can help drive your transformation process.