Bradley D.

Background

About Bradley D.

Senior computational chemist with 4+ years industry experience integrating cutting-edge AI platforms with molecular modeling to accelerate drug discovery. Combines deep technical expertise with strategic project leadership across 8+ oncology programs from hit identification to lead optimization. Current Impact at AstraZeneca: • Project Leadership: Accelerated first-in-class helicase program by 6 months through strategic implementation of novel Boltz co-folding models • Cross-functional Collaboration: Validated computational predictions with biophysics teams, establishing binding sites with high confidence across 3+ active programs • Team Development: Built institutional FEP/MD capabilities, mentored summer intern through novel kinetics workflow adopted across multiple programs • Portfolio Impact: Advanced 2+ compounds to preclinical development while achieving 100x solubility and 30-fold selectivity improvements Technical Expertise: • AI Platforms: AlphaFold, Boltz, ReInvent, AiZynth for structure prediction and generative design • Molecular Modeling: FEP calculations, molecular dynamics, quantum methods, enhanced sampling • Drug Discovery: Virtual screening, ADMET optimization, cheminformatics, QSAR • Programming: Python, RDKit, workflow automation, data visualization Key Achievements: • Strategic Planning: Defined computational chemistry strategy across 4+ discovery programs, establishing project-specific workflows that accelerated decision-making • Innovation Leadership: Pioneered AlphaFold implementation for complex targets, enabling biological assay development for 2 previously intractable programs • Process Optimization: Developed proprietary scoring workflows and high-throughput screening platforms, reducing costs while achieving 6% hit rates • Research Excellence: Published in top-tier journals with 400x efficiency methodologies, earning 2020 MRSEC Excellence Prize for open science leadership Passionate about bridging computational innovation with therapeutic impact through AI-driven drug discovery, advanced modeling techniques, and cross-functional collaboration. Specialties: AI-driven drug discovery, protein structure prediction, free energy perturbation, molecular dynamics, virtual screening, ADMET optimization