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A biochemistry-informed neural network for kinome selectivity prediction of small molecule inhibitors
Kinases regulate cellular activities. ML-based kinase selectivity prediction with XGBoost speeds up drug discovery by prioritizing compounds for testing.
We implemented Challenge 1 from Molecular Forecaster. This codebase implements a comprehensive machine learning pipeline for predicting kinase inhibitor binding affinities and selectivity profiles.
We built a RL-based Proximity Policy Optimization model to optimize Multiple Sequence Alignment (MSA) for AMR, outperforming traditional AI models. Faster, smarter, scalable.
From Undercooked Chicken for Dinner to Unstoppable Superbugs— Can AI Modeling Stop the Next AMR Outbreak?
project
Predicts affinity of inhibitors for cancerous tyrosine kinase
AMR sequence alignment
Align smarter, fight resistance faster.
PharmaHacks 2025 Challenge 1
Aligning genome sequences to identify similarities across DNA, RNA and protein sequences.
We developed a machine learning model to optimize sequence alignments, improving accuracy and speed while reducing computational costs, with potential applications in bioinformatics and drug discovery
Machine learning project that predicts Kd and of a molecule with a kinase and ranks molecules based on selectivity score
This is a new architecture of graph transformers that can define Mixture of Expert one being an optimistic expert and a pesssimistic expert for nuanced graph paths removal.
Multiple Sequence Alignment using Reinforcement Learning Agent
Multiple Sequence Alignment
Developing a machine learning model to accurately predict kinase selectivity profile of small molecule inhibitors through the ranking of kinases based on their given likelihood of being inhibited.
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