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platform passport · Review date not recorded

Chemistry42 / Pharma.AI

Generative chemistry that carried an AI drug into Phase II.

2/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using Chemistry42 / Pharma.AI?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forGeneration · Prediction
Evidence supportsPrimary links may be present, but BioAtlas does not claim a review date without a record-level timestamp.
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationIndependent reproducibility is limited by proprietary access.
Current registry recordVersion history not yet curated1 recorded release · Review date not recorded. A newer version is not assumed to be universally better.

What it is

Insilico's Pharma.AI stack spans target discovery (PandaOmics) and generative chemistry (Chemistry42), using GANs and reinforcement learning to invent novel, synthesizable molecules. Its lead candidate for idiopathic pulmonary fibrosis (rentosertib / INS018_055) — target and molecule both AI-derived — reached Phase II trials, a landmark for the field.

Evidence trail

BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.

Sources2 connectedPrimary resources and normalized claims
Claims0 normalizedNo normalized claim yet
EntityChemistry42 / Pharma.AIplatform · Version history not yet curated
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typeplatform
OrganizationInsilico Medicine
Platform introduced / founded2014
AccessProprietary
Commercial useVendor terms
DeploymentVendor managed
ComputeGPU or managed service
Domainschemistry
Biology → representation → computation → evidence

How Chemistry42 / Pharma.AI represents biology

platformchemistry

Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.

1 · Biological inputs
Molecular structures or discovery objectives
2 · Input representation
Molecular graph / tokens / 3D geometry
3 · Internal representation
Molecular representation
4 · Architecture
Chemistry model or platform
5 · Learning objective
Molecular prediction or generation
6 · Output representation
MoleculesCoordinatesScores

Biological scale

Modalities & tasks

MoleculeGenerationPredictionDocking

Registry, claims and frontier intelligence

Versioned registry

Version history not yet curated

1 version record · release year not yet normalized. Model-family identity remains separate from capability and access changes.

Explore version lineage →
Benchmark claim ledger

0 normalized claims

No task, dataset, split and metric claim has been normalized for this record yet.

Open claim intelligence →

Inputs and outputs

Inputs

Molecular structures or discovery objectives

Outputs

MoleculesScores or poses

Scientific and technical profile

Scientific principles

Generative adversarial networksReinforcement learningTarget identification from omics

Technology

GENTRL / GAN generatorsRL reward shapingSynthetic-accessibility scoring
Ideas before algorithms

Scientific lineage

Explore all foundations

These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.

Computational intelligence

Denoising diffusion generative models

Jascha Sohl-Dickstein, Jonathan Ho and collaborators

Modern protein-backbone, molecular-pose and biomolecular-complex generators use diffusion to sample valid three-dimensional structures and designs.

Matched concepts: generative, pose
Medicinal chemistry & pharmacology

Selective toxicity and the ‘magic bullet’

Paul Ehrlich

Target selectivity, therapeutic index and mechanism-based screening remain central goals of drug discovery.

Matched concepts: target
Medicinal chemistry & pharmacology

Rational antimetabolite drug design

Gertrude B. Elion & George H. Hitchings

Mechanism-based design, pathway selectivity and iterative medicinal chemistry are direct descendants of this strategy.

Matched concepts: candidate
Medicinal chemistry & pharmacology

Quantitative structure–activity relationships

Corwin Hansch

Classical QSAR established the central premise that molecular features can predict potency and guide optimization—the conceptual ancestor of modern molecular machine learning.

Matched concepts: molecule

Evaluation evidence

Dataset or evaluationNot yet curated
Task or metricNot yet extracted
Evidence statusPrimary paper linked; benchmark extraction pending
Open source ↗

BioAtlas has not yet extracted a structured benchmark claim for this record.

Known limitations

  • Independent reproducibility is limited by proprietary access.
  • A structured benchmark claim has not yet been extracted for this record.
  • Outputs require task-specific scientific and experimental validation.

Milestones

2014

INS018_055 was the first drug with AI-discovered target AND molecule in Phase II.

Evidence

Founded 2014, among the earliest AI-drug companies.