SMOL Drug Design

AI powered drug discovery platform for faster and cost-effective de novo small molecule design

Context

Time, effort, and cost intensive drug discovery process

(Upper) Chemical structures of small-molecule drugs (SMOLs), i.e. naltrexone, methotrexate, and doxorubicin. (Lower) Space-filling models of the SMDs: carbon (dark grey), hydrogen (light grey), nitrogen (blue), oxygen (red).
Structures and Models of Small Molecule Drugs

The discovery of New Chemical Entities (NCE) with the desired biological activity is the foundational step for new therapeutics. This scientific exploration involves dealing with the twin challenges of an expanding druggable target protein space for which the vast chemical space (encompassing >1065 drug-like molecule) is to be investigated. Such challenges demand sophisticated algorithms to computationally aid R&D in a high-throughput scenario. For the biopharma industry, this is particularly relevant as it already faces a serious concern with Avoidable Experiment Expenditure (AEE), estimated at approximately $48 billion annually.

Advancements in cheminformatics, computational chemistry, and the integration of AI/ML are paving the way for an informed exploration of the chemical space and pushing the boundaries “beyond the rule of 5” (bRo5) chemical space. In silico techniques, driven by these advances, are proving to be significant cost-saving accelerators for failing fast before experimenting in the wet lab. De novo molecular generation enables optimization for range of properties such as ADMET, synthesizability, and not just biological activity. These methods help the discovery chemists to explore a vast chemical space in shorter time frames and enhance the quality of leads generated.

Our Solution

Highly repeatable and accelerated in silico pipeline to generate de novo molecular leads for a given target protein

We help reduce AEE for biopharma by combining the concepts of cheminformatics, computational and generative chemistry with advances in AI/ML, Cloud & DevOps. Our solutions complement the traditional lab-based approaches with in silico solutions to accelerate drug discovery. Key features of our solution include:

  • Generative models to navigate the vast ‘drug-like’ chemical space while optimizing for biological activity, synthesizability, drug-like characteristics, ADMET properties
  • Fragment Based Drug Design (FBDD) that leverages generative AI for fragment linking, fragment growing and fragment merging in the context of a binding pocket
  • High throughput molecular docking to estimate binding affinity of lead candidates. Top docked complexes are further explored for potential interactions that reveal novel binding mechanisms through a curated binding site analysis
  • Accelerated MD to study protein-ligand interactions and estimate binding strength

The workflows/pipelines can be customized for target proteins and molecular properties of interest. High throughput docking pipelines can be deployed on cloud-based Kubernetes environments with schedulers such as SLURM for auto-scaling and workload management.

SMOL drug design pipeline steps: given a target protein the steps include Ligand Generator, Ligand Evaluator, ‘Drug Likeliness and Toxicity Filters, Automated High Throughput Docking and ADME characterization towards identifying Drug Leads for Wet Lab Assays
Small Molecule (SMOL) Drug Design Pipeline
Highlights

Key components & strengths

Automated pipeline for molecular library generation

Generating synthesizable drug-like molecules to distill promising candidates as drug seeds for downstream wet lab assays

State of the art AI/ML tools & techniques

Generative models combined with Reinforcement Learning (RL), and FBDD leveraging Generative AI

High throughput computational pipelines

Automated docking protocols, protein-ligand interaction analysis, DFT descriptors’ computations for ADMET models

Customizable and configurable components

Modules built leveraging open-source packages,customizable pipeline with flexibility to choose and add specific components

On-demand cloud computing infrastructure

HPC clusters, cloud-based Kubernetes environment for auto-scaling, and schedulers such as SLURM for workload management, resulting in high throughput
Outcomes

Accelerate discovery of de novo SMOL drugs

Faster and Cost-effective

Identification of synthesizable drug-like molecules with affinity to target protein and promising ADMET properties using advanced machine learning techniques and high throughput docking pipelines

Low-effort Customization

With modular and configurable solution components (such as Fragment library, Virtual screening, Retrosynthesis, ADMET prediction, MD simulations) across the molecular lead generation pipeline for different target proteins

Configurable and Scalable

Scalable computational resources with on-demand cloud-based High Performance Computing (HPC) clusters, infrastructure as a code (IaaC) approach and workload management techniques

Discover our offerings across the biopharma value chain

Learn more about our SMOL Drug Design