Insilico Medicine launches science MMAI gym to train frontier LLMs into pharmaceutical-grade scientific engines

(Press-News.org) New “AI GYM for Science” dramatically boosts the biological and chemical intelligence of any causal or frontier LLM, delivering up to 10x performance gains on key drug discovery benchmarks and advancing the company’s vision of Pharmaceutical Superintelligence (PSI).

CAMBRIDGE, Mass., January 22, 2026 – Insilico Medicine (“Insilico”, HKEX: 3696), a leading global AI-driven biotech company, today announced the launch of Science MMAI Gym, also branded as Insilico Medicine’s AI GYM for Science, a domain-specific training environment designed to transform any causal or frontier large language model (LLM) into a high-performance engine for real-world drug discovery and development tasks.

Building on more than a decade of AI research and its own internal pipeline of 27 preclinical candidates, 10+ molecules with IND clearance, and multiple Phase I and Phase IIa clinical trials completed or ongoing, Insilico is now opening its AI training infrastructure to external partners. Science MMAI Gym adapts and optimizes general-purpose LLMs, such as GPT, Claude, Gemini, Grok, Llama, Mistral and others, to reason in medicinal chemistry, biology, and clinical development with the precision required in modern pharma R&D.

Science MMAI Gym is a core component of Insilico’s long-term roadmap toward Pharmaceutical Superintelligence (PSI), with dedicated tracks for Chemical Superintelligence (CSI) and Biology/Clinical Superintelligence (BSI). Over a period of weeks to months, partner models “train” in the Gym using curated domain-specific reasoning datasets across multiple relevant domains and tasks, reward models, and reinforcement learning with domain-specific reasoning, and emerge with up to 10x improvements in performance on key chemistry and biology benchmarks, in some cases matching or approaching state of the art (SOTA) specialist models across multiple tasks, all with a single-model-does-it-all approach.

 

Boosting Frontier LLMs’ Biological and Chemical Intelligence

Despite their impressive general reasoning capabilities, flagship LLMs still underperform or fail on many mission-critical drug discovery tasks. As shown in benchmark comparisons assembled by Insilico, leading models struggle to accurately predict pharmacokinetic and toxicity endpoints, such as Caco-2 permeability, plasma protein binding, tissue distribution (VDss), half-life, microsomal and hepatocyte clearance, hERG and DILI risk, or LD50, when evaluated on the open Therapeutics Data Commons (TDC) benchmark suite. In several tasks, their mean absolute error (MAE) is orders of magnitude above practical SOTA targets, or their classification metrics, such as balanced accuracy (BA) and area under the ROC or precision-recall curve (AUROC/AUPRC), fall well below thresholds typically expected for preclinical decision-making.

The problem is not solved by better prompts alone. Even with 5 few-shot examples with in-context learning and rich task descriptions, general models often default to vague or chemically and biologically implausible reasoning. Simple fine-tuning on the training split of a benchmark can offer incremental improvements but rarely produces SOTA models which should operate robustly in the wild on out-of-distribution compounds and novel targets.

Science MMAI Gym addresses this gap directly. Rather than treating drug discovery as just another NLP benchmark, the Gym teaches LLMs domain-specific scientific reasoning, the language, formats, and conceptual chains that chemists, biologists, and clinicians actually use:

Medicinal and organic chemistry: multi-step optimization chains, reaction reasoning, retrosynthesis templates, structure-property relationships, and 3D binding interactions.

 Biology and target discovery: omics-aware reasoning over gene expression, pathways, disease mechanisms, and multi-objective target scoring.

 Clinical development: interpretation of trial designs, endpoints, response biomarkers, and prediction of success or failure of Phase 2 trials using proprietary benchmarks such as ClinBench.

On-demand high-quality reasoning data generation: the validated simulation/data generation models widely used in Chemistry42 and PandaOmics products generate as many reasoning traces as needed to teach the LLMs to reason, predict, create novel compounds, and optimize relevant properties.

 

Foundation Models for Drug Discovery: Insilico’s Starting Point

Science MMAI Gym is built on Insilico’s established portfolio of foundation models for chemistry and biology. These include:

 Natural Language and Chemistry LLM (Nach0 / Nach01), co-developed with NVIDIA a multi-domain encoder-decoder model trained on unlabeled scientific text, patents, molecule strings, and diverse chemistry datasets. It supports biomedical question answering, named entity recognition, molecular generation, reaction prediction, multi-step retrosynthesis, molecular property prediction, quantum property prediction, and cross-domain tasks such as description-to-molecule/protein and protein-to-description.

Proof-of-concept language models for 3D drug design, such as nach0-pc and BindGPT, published at AAAI 2025. These models demonstrated that LMs can perform shape- and pocket-conditioned 3D generation, linker design, scaffold decoration, conformer generation, and reinforcement-learning-driven 3D molecular design, at times outperforming specialized diffusion models.

These foundation models already operate on 2D and 3D small molecules, 1D and 3D protein structures, and multiple cross-modal tasks. Science MMAI Gym generalizes and industrializes this work: instead of a single research model, it offers a systematic environment where any causal LM can be adapted into a domain-specific scientific copilot.

 

How Insilico’s Models Compare to Frontier LLMs

Insilico’s internal benchmarking shows that its specialized models consistently outperform general LLMs on drug discovery tasks:

On TDC ADMET tasks, Insilico’s Nach01-1B-1 model for chemistry achieves substantially lower MAE for key properties like lipophilicity and solubility, and higher Spearman correlations for clearance and half-life, compared with general LLM-based approaches. Spearman measures how well predicted rankings match true orderings – critical in early-stage triaging where relative ordering of compounds often matters more than exact values.

On TargetBench 1.0, an open benchmark for target identification, Insilico’s TargetPro, built on its PandaOmics platform, demonstrates superior retrieval of clinically validated targets among the top-ranked predictions, significantly outperforming general LLMs across multiple diseases.

Earlier this year, Insilico introduced TargetBench 1.0 and TargetPro to encourage transparent, reproducible evaluation of AI systems in biology. Science MMAI Gym extends that philosophy from target discovery into a comprehensive training and benchmarking environment covering chemistry, biology, and clinical reasoning.

 

Introducing Science MMAI Gym: Can We Teach Any LLM Drug Discovery?

Science MMAI Gym directly tackles the question: Can we take any LLM including those that initially fail most drug discovery tasks – and train it into a high-performing scientific engine?

The Gym is architected as a multi-stage training regime for any causal LM, including open-source models such as Qwen, Llama, Mistral and others, as well as customer-owned or proprietary models:

1.     High-quality, domain-specific reasoning datasets

Medicinal chemistry reasoning: over 4 million optimization chains, 700+ medchem structural rules, and curated examples linking structural changes to ADMET and potency outcomes.

Organic synthesis reasoning: more than 100 million reactions and industrial organic chemistry synthesis descriptions, plus 3,000+ retrosynthesis templates and tags.

DMPK and toxicity: 100+ predictive models for pharmacokinetics and 200+ models for toxicity and off-target selectivity.

3D information: hundreds of thousands to millions of molecular dynamics (MD) trajectories, protein pockets, and docking/simulation outputs that ground the model’s reasoning in geometry and physics.

2.     Multi-task Supervised Fine-Tuning (SFT) + Reinforcement Fine-Tuning (RFT)

Models undergo multi-task SFT with task and domain-specific reasoning to learn diverse tasks simultaneously, followed by offline and online reinforcement learning to hone reasoning skills using experimental or high-quality data generated by validated  reward models. For example, rewards that increase when molecules stay drug-like, retain the scaffold, and improve multiple properties at once, or when trial-outcome predictions align with experimental  clinical results.

3.     Data decontamination and robust benchmarking

An automated data decontamination system checks for overlap between training and test splits, removing leakage across all tasks, including retrosynthesis (where multi-step data that contained public test sets were excluded).

Each training cycle is evaluated against public and in-house out-of-distribution (OOD) benchmarks, including TDC, MuMO-Instruct, FGBench, USPTO and internal retrosynthesis, ADMET, MedChem datasets, TargetBench, and ClinBench.

In a typical engagement, a customer provides their base model. Over 1–3 months of “membership” at Science MMAI Gym, Insilico runs the full curriculum and returns an updated model with substantially improved performance, along with detailed benchmark reports and optional wet-lab validation.

 

Chemistry Superintelligence (CSI): Qwen3-14B Case Studies

A central CSI case study in the slide deck follows Qwen3-14B, an open-source causal LLM:

Before Science MMAI Gym

Qwen3-14B, used with five-shot in-context learning, fails completely on roughly 70% of medchem benchmarks, and performs poorly on many TDC ADMET tasks and retrosynthesis metrics. In several tasks, its errors are far beyond SOTA goals, and it scores near zero on single-step retrosynthesis quality as measured by Insilico’s ChemCensor metric, which assesses the plausibility and selectivity of proposed reactions on a 0–5 scale.

After two weeks at Science MMAI Gym

The Qwen3-14B-MMAI variant solves over 95% of medchem benchmarks and becomes a “single-model-does-it-all” chemistry engine:

Achieves SOTA or near-SOTA performance on 4 of 22 ADMET tasks in the TDC suite against task-specific specialist models, while beating TxGemma-27B-Predict – a strong category-specific generalist – on 12 of 22 tasks in a single SFT+RFT run.

Delivers SOTA Success Rate on 5 of 5 optimization tasks in the MuMO-Instruct benchmark, while maintaining high structural similarity (0.5–0.6) to the starting molecules, ensuring modifications remain realistic analogs rather than property-hacking artifacts.

Outperforms ether0, a reinforcement-learning-tuned domain generalist LLM but based on general reasoning as opposed to domain-specific, in single-step retrosynthesis on both standard USPTO-50k and out-of-distribution in-house expert datasets according to ChemCensor-based metrics.

Qualitatively, the “before and after” chat transcripts show that base models tend to generate vague, non-specific rationales and incorrect chemistry, whereas post-Gym models internalize domain reasoning: they parse SMILES and 3D information, decompose structures, explain ADMET liabilities, propose rational modifications, and even generate plausible 3D binding poses in the reasoning traces, often achieving SOTA without external tool calls.

 

Biology and Clinical Superintelligence (BSI): TargetBench and ClinBench

Science MMAI Gym for Biology builds on one of the largest integrated biology and clinical datasets assembled in the industry:

1.3M+ omics samples across ~1,000 diseases, including RNA-seq, microarrays, single-cell data, ATAC-seq, proteomics, GWAS/EWAS, and more.

7M+ gene–disease associations, manually curated and AI-aggregated.

47M+ scientific documents, including publications, patents, grants, and clinical trial records.

300K+ patients with longitudinal EHR data, including partially mortality-linked cohorts.

These data power multiple reasoning tasks: target identification, indication prioritization, trial outcome prediction, treatment response modeling, biomarkers for survival and therapy response, and aging biology, supported by PandaOmics scores, 700+ “golden projects,” Precious-GPT aging clocks, and TargetPro novel target scorers.

Two BSI case studies illustrate the Gym’s impact:

1.     ClinBench – Predicting Phase 2 Clinical Trial Outcomes

A base Qwen3-4B model begins with an F1 score of ~0.82 on predicting success vs failure of Phase 2 trials whose results were published after January 1, 2025.

After SFT and GRPO-based reinforcement training at MMAI Gym, the model’s F1 score rises to 0.94, with accuracy of 0.92 and recall of 1.00 – outperforming a broad set of frontier LLMs, including several widely used commercial models that cluster around F1 scores of 0.82–0.87 on the same benchmark. F1 balances precision and recall, making it a practical summary of overall classification quality for unbalanced clinical datasets.

2.     TargetBench – Clinical Target Retrieval and Novel Target Quality

Starting from a weak baseline, Qwen3-1.7B fine-tuned with SFT and GRPO at MMAI Gym climbs to the top composite ranking for novel target identification across multiple diseases, outperforming frontier LLMs on multiple metrics such as the percentage of: novel targets with known protein structures, targets considered druggable, linked to approved drugs, average pathway relevance scores, number of available bioassays, and number of known gene modulators – all indicators of biological plausibility and translational readiness.

Together, these results suggest that Science MMAI Gym can turn a general LLM into a biology- and clinic-savvy copilot, capable of assisting with tasks like target triage, indication expansion, trial design, and biomarker discovery.

 

Towards Pharmaceutical Superintelligence (PSI)

The Pharmaceutical Superintelligence (PSI) vision described in the slide deck positions Science MMAI Gym as a continuous training environment:

 LLMs are first evaluated through benchmarking across chemistry and biology tasks.

Molecular and biological datasets are curated and harmonized, and tasks and benchmarks are selected to reflect real research settings.

Models undergo multi-day MMAI Gym training sprints, combining multi-task SFT, reasoning-aware RL from AI feedback (RLAIF), and architecture selection.

Processed learnings feed into ongoing iterations, gradually building CSI and BSI pillars that together form PSI – a model (or model family) capable of supporting end-to-end drug discovery and development workflows.

Partners can move from a generic baseline LLM to a PSI-tuned model stack adapted to their proprietary data and R&D strategies, with optional wet-lab validation through Insilico’s automated assay platforms.

 

Business Model: “Membership” in the AI GYM for Science

Science MMAI Gym is offered as a membership-style program:

Flexible terms – from intensive two-week or one-month sprints to three-month or longer PSI-oriented engagements.

One-time or recurring subscriptions, where Insilico runs end-to-end training, benchmarking, and validation cycles, tailored to the partner’s priorities (chemistry-centric CSI, biology/clinical-centric BSI, or full PSI).

At the end of a membership cycle, partners receive a significantly upgraded model – a CSI/BSI/PSI-enhanced version of their original LLM with materially improved performance on internal and external benchmarks. Additionally they receive detailed benchmark reports covering public suites (e.g., TDC, MuMO-Instruct, FGBench, TargetBench, ClinBench, USPTO-50k) and, where appropriate, out-of-distribution in-house benchmarks.Optional wet-lab validation packages where model-generated hypotheses (e.g., novel targets or optimized molecules) are tested in Insilico’s experimental platforms are also included.

The overarching promise is “up to 10x performance improvement compared to baseline”, and substantial gains even relative to specialist models on multiple tasks – translating to more reliable toxicity predictions, better target triage, and more accurate trial outcome forecasts.

Insilico invites pharma and biotech companies, AI labs, and cloud providers to “bring your AI model to Science MMAI Gym” and explore CSI, BSI, and PSI memberships tailored to their R&D pipelines.

 

Forward-Looking Statements:

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About Insilico Medicine

Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend health longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, under the stock code 03696.HK.

By integrating AI and automation technologies and deep in-house drug discovery capabilities, Insilico is delivering innovative drug solutions for unmet needs including fibrosis, oncology, immunology, pain, and obesity and metabolic disorders. Additionally, Insilico extends the reach of Pharma.AI across diverse industries, such as advanced materials, agriculture, nutritional products and veterinary medicine. For more information, please visit www.insilico.com

 

 

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