Blog | AI Drug Discovery News & Insights

12 February, 2026

Predicting Drug Toxicity Early: Role of AI in Preclinical Development

Drug toxicity remains one of the leading reasons for attrition during preclinical and early clinical development. Compounds that appear promising in efficacy often fail when...

30 January, 2026

AI in Drug Discovery: From Target Identification to Clinical Success

Drug discovery is still defined by long timelines, rising costs, and a high probability of failure. Despite scientific advances, nearly 9 out of 10 drug candidates fail during clinical development...

31 October, 2025

Drug Discovery with SMAG’s Human Intestinal Absorption (HIA) Prediction Model

Every new drug candidate carries promise — but also risk. One of the greatest challenges in drug discovery is identifying compounds that may be harmful to human cells before...

28 October, 2025

HEK-293 Cytotoxicity Prediction: Strengthening Early Safety in Drug Discovery

Every new drug candidate carries promise — but also risk. One of the greatest challenges in drug discovery is identifying compounds that may be harmful to human cells before...

01 October, 2025

MDCK Cell Permeability Prediction: A Smarter Way to Support Drug Discovery

Predicting how a potential drug behaves inside the body is one of the most critical challenges in early drug development. Among the key properties, cell permeability determines...

25 September, 2025

Advancing Drug Discovery with AI: SMAG’s High-Performance A549 Cytotoxicity Prediction Model

In the journey of drug discovery, one of the most critical hurdles researchers face is toxicity. Cytotoxicity, the ability of a compound to cause cell death, often becomes a bottleneck in preclinical and clinical stages...

19 September, 2025

SMAG’s VDss Prediction Model: Advancing Pharmacokinetics in Drug Discovery

Accurate pharmacokinetic (PK) predictions are essential in modern drug discovery, where every decision impacts safety, efficacy, and development timelines.

16 September, 2025

AI-Driven CYP2C8 Inhibition Model for Drug Discovery

Drug–drug interactions (DDIs) remain a persistent challenge in modern medicine, often leading to adverse effects, reduced therapeutic efficacy, or even life-threatening outcomes. One of the critical factors behind such interactions.

8 September, 2025

Enhancing Drug Discovery with Smarter Plasma Protein Binding Predictions

In our continuous effort to streamline and strengthen early drug discovery, SMAG has developed a Plasma Protein Binding (PPB) Prediction Model...

smag-ai-drug-repurposing-platform-update

18 August, 2025

Advancing Smarter drug repurposing through knowledge graphs & machine learning

SMAG has advanced its internal drug repurposing research efforts with the development of version 2.1 of its integrated knowledge graph and computational analysis framework...

smag-ai-admet-pgp-predictive-toxicology-tool

31 July, 2025

Introducing SMAG’s ADMET P‑Glycoprotein Substrate Prediction Tool

SMAG is proud to unveil the launch of its P‑Glycoprotein (PGP) Substrate Prediction Tool, now integrated into our platform’s ADMET module...

03 July, 2025

AI in Predictive Toxicology for Safer Drug Development | SMAG AI

Predictive toxicology—the use of computational approaches to forecast adverse effects in drug candidates—is now critical in modern pharmaceutical research...

20 June, 2025

Integrating QSAR Models in Early-Stage Drug Development

Quantitative Structure–Activity Relationship (QSAR) models have become indispensable tools in modern drug discovery, particularly during the early stages of development...

02 May, 2025

Molecular Docking Simulations Enhanced by AI

Molecular docking simulations are a cornerstone of modern drug discovery, providing insights into how a drug molecule interacts with a disease-causing target protein...

Why Drug Discovery Is So Slow and How AI Is Speeding It Up - SMAG

17 March, 2025

Why Drug Discovery Takes So Long—and How AI is Speeding It Up

Scientists have spent a decade researching a cure for a rare disease. The lab conducted countless studies, faced difficult setbacks, and followed strict restrictions