Third Quarter 2026 Financial Highlights (as compared to third quarter 2025)

• Total revenue increased 7% to $21.9 million
• Software revenue was flat at $12.6 million, representing 58% of total revenue
• Services revenue increased 20% to $9.3 million, representing 42% of total revenue
• Gross profit was $15.1 million and gross margin was 69%, compared to $13.0 million and 64%
• Net income of $3.6 million and diluted earnings per share of $0.18, compared to net loss of $67.3 million and diluted losses per share of $3.35
• Adjusted EBITDA of $7.9 million, representing 36% of total revenue, compared to $7.4 million, representing 37% of total revenue
• Adjusted net income of $6.1 million and adjusted diluted EPS of $0.30 compared to adjusted net income of $9.0 million and adjusted diluted EPS of $0.45

Nine Months 2026 Financial Highlights (as compared to nine months 2025)

• Total revenue increased 5% to $64.6 million
• Software revenue decreased 2% to $36.1 million, representing 56% of total revenue
• Services revenue increased 14% to $28.5 million, representing 44% of total revenue
• Gross profit was $42.2 million and gross margin was 65%, compared to $36.4 million and 59%
• Net income of $8.8 million and diluted earnings per share of $0.43, compared to net loss of $64.0 million and diluted losses per share of $3.19
• Adjusted EBITDA of $20.2 million, representing 31% of total revenue, compared to $18.5 million, representing 30% of total revenue
• Adjusted net income of $15.7 million and adjusted diluted EPS of $0.78, compared to $18.7 million and adjusted diluted EPS of $0.93

Management Commentary

“We delivered solid third quarter results, with revenue increasing 7%, highlighted by strength in our services revenue, which grew 20%, while software revenue was flat year over year,” said Shawn O’Connor, Chief Executive Officer of Simulations Plus. “Our performance reflects the resilience of our business model and the value our solutions provide to clients across the drug development lifecycle.”

“Subsequent to quarter end, on June 15, 2026, we entered into a definitive merger agreement to be acquired by affiliates of Altaris, LLC (“Altaris”). We believe the transaction better positions Simulations Plus to further advance its scientific leadership and expand the impact of our model-informed and AI-enabled solutions. As we move toward the expected closing in the fourth quarter of calendar 2026, we remain focused on delivering for our clients and executing at a high level throughout this transition.”

Non-GAAP Financial Measures

This press release contains “non-GAAP financial measures,” which are measures that either exclude or include amounts that are not excluded or included in the most directly comparable measures calculated and presented in accordance with U.S. generally accepted accounting principles (“GAAP”).

A further explanation and reconciliation of these non-GAAP financial measures is included below and in the financial tables in this release.

The Company believes that the non-GAAP financial measures presented facilitate an understanding of operating performance and provide a meaningful comparison of its results between periods. The Company’s management uses non-GAAP financial measures to, among other things, evaluate its ongoing operations in relation to historical results, for internal planning and forecasting purposes, and in the calculation of performance-based compensation. Adjusted EBITDA and Adjusted Diluted EPS represent measures that we believe are customarily used by investors and analysts to evaluate the financial performance of companies in addition to the GAAP measures that we present. Our management also believes that these measures are useful in evaluating our core operating results. However, Adjusted EBITDA and Adjusted Diluted EPS are not measures of financial performance under accounting principles generally accepted in the United States of America and should not be considered an alternative to net income, operating income, or diluted EPS as indicators of our operating performance or to net cash provided by operating activities as a measure of our liquidity. We believe the Company’s Adjusted EBITDA and Adjusted Diluted EPS measures provide information that is directly comparable to that provided by other peer companies in our industry, but other companies may calculate non-GAAP financial results differently, particularly related to nonrecurring, unusual items.

Please note that the Company has not reconciled the adjusted EBITDA or adjusted diluted earnings per share forward-looking guidance included in this press release to the most directly comparable GAAP measures because this cannot be done without unreasonable effort due to the variability and low visibility with respect to costs related to acquisitions, financings, and employee stock compensation programs, which are potential adjustments to future earnings. We expect the variability of these items to have a potentially unpredictable, and a potentially significant, impact on our future GAAP financial results.

Adjusted EBITDA

Adjusted EBITDA represents net income excluding the effect of interest expense (income), provision (benefit) for income taxes, depreciation and amortization, equity-based compensation expense, loss (gain) on currency exchange, impairment charges, change in fair value of contingent consideration, reorganization expense, acquisition and integration expense, and other items not indicative of our ongoing operating performance.

Adjusted Net Income and Adjusted Diluted EPS

Adjusted net income and adjusted diluted earnings per share exclude the effect of amortization, equity-based compensation expense, loss (gain) on currency exchange, impairment charges, change in fair value of contingent consideration, reorganization expense, acquisition and integration expense, and other items not indicative of our ongoing operating performance as well as the income tax provision adjustment for such charges.

The Company excludes the above items because they are outside of the Company’s normal operations and/or, in certain cases, are difficult to forecast accurately.

View full results here. 

Evaluation of drug-like properties in vitro is critical in identifying viable candidates for new central nervous system (CNS) drugs. Excitatory amino acid transporter 2 (EAAT2) is the primary glutamate transporter in the brain, responsible for approximately 90% of synaptic glutamate clearance. Its dysfunction leads to excessive glutamate accumulation and excitotoxic neuronal death, contributing to disorders including epilepsy, amyotrophic lateral sclerosis, and Alzheimer’s disease. Positive allosteric modulation of EAAT2 represents a promising therapeutic strategy to restore glutamate homeostasis. This study aimed to evaluate in vitro drug-like properties of two novel positive allosteric modulators of EAAT2, (R)-AS-78 and (R)-PC-2, and to assess their potential for CNS drug development. PAMPA assay and bidirectional Caco-2 assay were used to evaluate membrane permeability and P-glycoprotein efflux liability. Rapid equilibrium dialysis was used to determine plasma protein binding. Metabolic stability and metabolite identification were assessed by incubation with human liver microsomes and LC-MS/MS analysis. CYP3A4 inhibitory potential was evaluated using the P450-Glo luminescence assay, and cytotoxicity was assessed using the MTS assay in HepG2 and SH-SY5Y cell lines. ADMET Predictor and MetaSite software predictions supplemented the experimental results. The results indicate that both compounds demonstrate drug-like properties suitable for further development as CNS drug candidates, with acceptable permeability, plasma protein binding and metabolic stability, no significant CYP3A4 inhibition, and no cytotoxicity. This is important as inadequate drug-like properties remain a leading cause of failure in CNS drug development.

By Mugisha Sambazi Kevin

The initiative expands the companies’ collaboration announced in May 2026, extending their work in GPU-accelerated simulation and AI-assisted modeling workflows into agentic drug development. Together, the companies are combining AI reasoning, scientific literature, validated computational engines, and scalable compute within a scientific workflow.

The announcement coincides with NVIDIA’s introduction of the BioNeMo Agent Toolkit at the BIO International Convention.

The integration is initially focused on two areas:

Grounding scientific reasoning in trusted evidence

Composer agents are expected to leverage NVIDIA Nemotron™ Parse to extract and structure information from scientific literature, enabling agents to retrieve relevant evidence while maintaining provenance back to the original source. Researchers can trace recommendations and conclusions to the supporting literature and data.

Accelerating quantitative systems pharmacology at scale

The companies are together advancing nvQSP, a collaborative development initiative focused on CUDA-optimized ordinary differential equation (ODE) solvers for quantitative systems pharmacology (QSP). By accelerating computationally intensive simulations on NVIDIA accelerated computing infrastructure, nvQSP enables scientists to explore larger parameter spaces, evaluate more hypotheses, and iterate more rapidly across complex biological systems. nvQSP builds on the companies’ previously announced work to accelerate computationally intensive QSP workflows using GPU-optimized simulation technology.

These capabilities are being incorporated into Composer, allowing agents to coordinate scientific workflows while remaining rooted in validated computational engines and source-based evidence.

“AI can help scientists explore more possibilities, but in drug development those insights must remain reproducible, explainable, and grounded in validated science,” said Erik Guffrey, Co-Chief Product and Technology Officer of Simulations Plus. “By combining the NVIDIA BioNeMo Agent Toolkit with our scientific engines, we’re building agents that seek to accelerate scientific work while preserving the rigor required for critical development decisions.”

The initiative reflects Simulations Plus’ strategy to connect AI reasoning, scientific knowledge, and validated computational engines within a unified environment for model-informed drug development.

“We believe agentic AI represents an important evolution in how scientists interact with models, data, and scientific knowledge,” said Shawn O’Connor, Chief Executive Officer of Simulations Plus. “Our goal is to help researchers move from question to insight more efficiently while keeping validated science at the center of decision-making.”

So the question on your mind is not whether agentic AI is coming. It is this: can you stake a regulatory submission on it?

That is the question this piece is about, because it is the one that matters most in our industry. A drug program is a chain of decisions that stretches over years and absorbs hundreds of millions of dollars. Every link in that chain has to be defensible—to a regulator, to an internal quality team, to the next scientist who picks up the model two years from now and needs to understand exactly how a conclusion was reached. An AI agent that produces a brilliant insight you cannot reproduce, cannot audit, and cannot trace back to a validated computation becomes a liability in that world, however impressive the insight.

The questions that separate trustworthy agentic AI from the rest

If you are evaluating agentic AI for regulated science, four questions tell you most of what you need to know:

• What actually produces the result—a language model, or a validated scientific engine?
• Can the result be reproduced—exactly, every time, from the same inputs?
• Can every decision be traced and replayed—months or years later, by someone who wasn’t there?
• Does human expertise stay accountable—with the scientist directing the work, not deferring to it?

These are the questions we built Composer, our AI-native platform for model-informed drug development, to answer. At its heart is an AI co-scientist: an agent that reasons about the scientific problem and orchestrates our validated engines through natural language, while the scientist stays in control. Here is how.

Our thesis: the insight is only as trustworthy as the engine beneath it

The conviction at the center of how we build, and the thing our customers respond to most strongly: an AI-generated insight is only as trustworthy as the validated engine that produced it.

A large language model can summarize a mechanism, propose a hypothesis, even sketch a model structure. What it cannot do—and should never be asked to do—is be the science. The numerical simulation, the parameter estimation, the mechanistic ODE solver that a drug decision rests on must come from an engine that has been validated against reference models and benchmark datasets, that produces the same output every time it is run, and that can be audited independently of any AI in the loop. A deterministic, validated engine is a world away from a chatbot dressed up as science.

So in the Composer ecosystem, the AI orchestrates the engine rather than replacing it. The co-scientist is designed to reason about the problem and decide what to do; the validated engine does the computing. We keep a clean line between the reasoning layer, which is probabilistic and fast-moving, and the computation layer, which is deterministic and qualifiable. The AI is intended to be an enhancement—a powerful one—but it is deliberately kept off the regulatory critical path. Every engine in Composer remains fully usable, and fully validatable, without any AI at all.

That architecture is what lets us answer the second and third questions directly.

Reproducibility. Run the same analysis with the same inputs and you get the same result—because the work is done by deterministic engines, not by a model that improvises. When a scientist is ready to move from open-ended exploration to a standardized process, the co-scientist can capture the entire interaction as a deterministic Workflow: an inspectable, versionable artifact that runs the same way every time, with no AI in the execution path. Exploration becomes production without losing rigor.

Replay-ability. Every analysis leaves a complete, traceable record—which engine version ran, with which parameters, producing which result, leading to which decision. A colleague, a regulator, or the same scientist two years later can replay the reasoning end to end. The trail is the explanation. In a field where “right for the wrong reasons” is a genuine scientific failure mode, the ability to show your work is part of the science itself, well beyond any compliance checkbox.

For three decades, our engines—GastroPlus®, MonolixSuite™, ADMET Predictor®, and our mechanistic QSP platforms—have carried exactly this burden of proof inside regulatory filings around the world. They are the foundation the agents stand on.

Why the BioNeMo Agent Toolkit is foundational to this

A validated foundation answers the trust question. But the agents that orchestrate that foundation also need to be genuinely fluent in biology, not merely fluent in language. This is exactly where the NVIDIA BioNeMo Agent Toolkit becomes foundational to our strategy.

General-purpose reasoning gets an agent surprisingly far. But drug development runs on domain-specific understanding: the structure of a protein, the pharmacology of a target, the patterns buried in genomics and clinical data, the mechanistic logic of a disease. The BioNeMo Agent Toolkit—NVIDIA’s life-sciences agent stack, including Nemotron models, NemoClaw blueprints, and OpenShell secure runtime—provides domain-specific models, NVIDIA NIM microservices, libraries, and frameworks built for life sciences. It is what lets an agent act on biological data rather than merely describe it, and it is what bridges general-purpose AI reasoning with real-world biological computation.

This is the combination that matters: our validated engines seek to bring scientific truth that holds up under scrutiny; the BioNeMo Agent Toolkit brings biological breadth and acceleration. Neither is sufficient alone, and the scientist directs both.

How we are using the BioNeMo Agent Toolkit today—and where we are going

The collaboration is already underway across our ecosystem on three fronts.

Grounded in evidence. Our agents are incorporating NVIDIA Nemotron Parse for scientific literature parsing and extraction—pulling parameters, mechanistic relationships, and quantitative data out of the dense PDFs, tables, and figures that hold the field’s accumulated knowledge. Extraction quality is our goal: a misread table becomes a bad parameter becomes a flawed model. By grounding our agents’ knowledge bases in high-fidelity extraction—with provenance back to the source—we let the co-scientist reason over what the literature actually says and let the scientist verify the evidence behind any recommendation. This is how an agent earns the right to propose a parameter rather than hallucinate one.

Accelerated by computation. Together with NVIDIA, we are collaborating on nvQSP to bring CUDA-optimized ODE solvers to our mechanistic modeling engines—GPU-accelerated QSP simulation that reduces the time to evaluate virtual populations and explore competing hypotheses. The science of a QSP or PK/PD model lives in systems of differential equations, and solving them is the computational bottleneck. GPU-accelerated solvers return the same answer the CPU would, while changing what becomes possible: larger virtual populations, broader parameter-space exploration, agentic search across many candidate models in the time it used to take to run one. Faster computation does not change the science; it expands the number of scientific questions a team can afford to ask.

Expanded by specialized biological models. Our vision goes further, and we are building toward it deliberately. We intend to host NVIDIA’s frontier biological models directly within the Composer ecosystem and to power a new generation of Composer Agents on BioNeMo Agent Toolkit infrastructure—new classes of biological reasoning available to the co-scientist as first-class, orchestratable tools, sitting right alongside our validated simulation engines. Every one of those new capabilities inherits the same discipline as the rest of the platform: scoped, version-traceable, and validated for what it is claimed to do, with the AI layer kept off the critical path. Biological frontier models and decades-validated engines, in one ecosystem, under one standard of trust.

What this means for scientists and for discovery

Strip away the architecture and the partnership mechanics, and here is what changes for the person doing the work — which brings us to the fourth question, the one about human accountability.

Agentic AI removes friction from the process, not the scientist from the science. Hypotheses get generated and tested faster, because the distance between “I have an idea” and “I have a result” collapses from weeks to hours. Experimental scientists who were never going to become expert modelers can now direct sophisticated modeling through natural language — the expertise is in the platform, available to them. And the collaboration between human scientist and AI agent becomes a real partnership rather than a novelty, because every recommendation remains inspectable, reproducible, and subject to human review. Scientific accountability stays exactly where it belongs: with the experts directing the work.

That is the future we are building toward: agentic science that moves at the speed of AI and holds up to the scrutiny of a regulator, in the same breath.

Agentic drug development needs a foundation scientists can trust. We have spent thirty years building the validated engines that scientific truth rests on; NVIDIA BioNeMo Agent Toolkit extends its reach into new domains of biology; and the scientist stays firmly at the controls of both. That is the partnership—and the standard—we are bringing to BIO 2026.

By Erik Guffrey, Co-Chief Product & Technology Officer