Next-Generation Molecular AI

Revolutionary RL-GAN Architecture for Molecular Generation

Train custom generative models from your SMILES datasets. Harness three-stage reinforcement learning with actor-critic architecture to explore, optimize, and expand chemical space with unprecedented precision and control.

3-Stage

RL Framework

Policy-Gradient

Optimization

Multi-Parameter

Objectives

Custom Model Training

Train fully custom molecular generative models directly from your SMILES datasets with learned SAR patterns

RL-GAN Hybrid

Generator as actor, discriminator as critic—dynamic evolution through closed adaptive feedback loops

Property-Driven Discovery

SMARTS constraints, reward functions, and negative datasets for precise chemical space navigation

Patent Intelligence

Integrated patent database checking for novelty assessment and freedom-to-operate analysis

Three-Stage Reinforcement Learning Framework

A paradigm shift in de novo molecular design through policy-gradient optimization and actor-critic architecture

Explore

The generator network (actor) learns scaffold distributions and SAR patterns from input SMILES datasets, establishing a foundation for controlled molecular construction through initial policy formation.

Transformer Architecture Scaffold Learning SAR Mapping

Optimize

The discriminator network (critic) evaluates generated molecules against property-based reward functions, penalizing toxic chemotypes and enforcing SMARTS-level structural constraints through continuous feedback.

Reward Engineering SMARTS Filtering ADMET Scoring

Expand

Policy-gradient optimization propagates critic feedback to the generator, refining sampling strategy in an adaptive loop. This produces high-volume libraries of novel, valid, drug-like molecules optimized for multi-parameter objectives.

Policy Gradient Adaptive Sampling Library Generation

Molecular Generation Workflow

From dataset to discovery: streamlined pipeline for property-driven molecular design

Janak transforms molecular generation into a systematic, AI-driven process. Starting with your proprietary SMILES datasets, the platform learns chemical patterns and structure-activity relationships, then applies reinforcement learning to iteratively generate and optimize molecules.

The actor-critic architecture ensures that every generated molecule is evaluated against your specific objectives—whether potency, selectivity, drug-likeness, or ADMET properties—while maintaining synthetic feasibility and novelty.

Data-Driven Learning

Train on your SMILES datasets to capture proprietary chemistry and SAR insights unique to your research

Reward-Guided Generation

Define custom reward functions encoding potency, selectivity, ADMET, and other critical molecular properties

Intelligent Filtering

Apply SMARTS constraints and negative datasets to exclude unwanted chemotypes and ensure drug-likeness

Novelty Checking

Automated patent database screening ensures generated molecules are novel and commercially viable

Advanced Capabilities

Comprehensive tools for controlled, property-driven molecular generation

Transformer-Based Architecture

Leverages state-of-the-art transformer models (including Taiga-inspired architectures) for superior sequence learning and molecular grammar understanding, enabling generation of chemically valid, synthesizable structures.

Multi-Parameter Optimization

Simultaneously optimize across multiple objectives including potency, selectivity, ADMET properties, synthetic accessibility, and patent freedom—all within a unified reward framework.

SMARTS-Level Constraints

Define precise structural requirements and exclusions using SMARTS patterns, ensuring generated molecules conform to your exact chemical specifications and avoid problematic substructures.

Negative Dataset Penalization

Train the model to actively avoid toxic, reactive, or unwanted chemotypes by incorporating negative examples that penalize generation of undesirable molecular features.

High-Volume Library Generation

Generate thousands to millions of novel molecules efficiently, with built-in diversity mechanisms ensuring comprehensive coverage of the optimized chemical space.

Seamless Export & Integration

Export generated molecules in standard formats (SMILES, SDF, MOL) for immediate use in downstream workflows including docking, virtual screening, and synthesis planning.

Why Janak Represents a Paradigm Shift

Bridging generative modeling with rational, property-driven lead discovery

Actor-Critic Architecture

The generator acts as the actor, iteratively constructing molecules through a policy that maximizes long-term reward, while the discriminator functions as the critic, distinguishing synthetically feasible, high-quality candidates from inferior ones.

Task-Specific Reward Functions

Quantitatively encode objectives such as potency, selectivity, drug-likeness, or ADMET suitability. The reward function guides learning toward high-value regions of chemical space with precision and reproducibility.

Closed Adaptive Loop

Policy-gradient optimization continuously refines the generator's sampling strategy based on critic feedback, enabling dynamic evolution of chemical motifs toward multi-parameter optimization goals in real-time.

Discrete Structure Generation

Addresses the core difficulty of generating discrete molecular structures while simultaneously optimizing for chemical and biological desirability—a challenge that traditional generative methods struggle to solve.

Chemical Space Navigation

Systematically explore and expand desired chemical space through learned scaffold distributions and SAR relationships, discovering novel molecular architectures beyond traditional medicinal chemistry intuition.

Validated Methodology

Built on peer-reviewed research and validated by advanced architectures like Taiga, demonstrating the effectiveness of RL-GAN approaches for controlled molecular generation with desired properties at scale.