Computational Modeling Overview

Principles

Definition

Computational modeling is a research methodology employing computer simulations and algorithms to construct explicit representations of complex systems or theoretical frameworks. Its significance lies in enabling the exploration of dynamics, prediction of outcomes, and testing of hypotheses across diverse scientific and scholarly domains.

Ontological type

Core Methods

Model Validation

Applications

Key developments

Key Figures

Implicit Solvent Modeling era

Arthur J. Olson [1] is associated with Washington University in St. Louis [3] and Pennsylvania State University [4] during the Implicit Solvent Modeling era. His key contributions in this era include automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function [7], which advanced docking efficiency and reliability in implicit-solvent workflows. Peter A. Kollman [2] is associated with Harvard University [5] and the University of California, San Francisco [6] during this era. His key contributions include the weighted histogram analysis method for free-energy calculations on biomolecules, as established in the 1992 paper The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method [8], and its 1995 multidimensional extension Multidimensional free-energy calculations using the weighted histogram analysis method [9], which enabled robust, scalable free-energy estimates and multidimensional analyses essential for implicit-solvent modeling.

Multiscale Integrative Modeling era

Richard A. Friesner[1] was a central figure in this era, with affiliations at the Massachusetts Institute of Technology[3] and the University of California, San Francisco[4]. His key contributions include Jaguar, a high‐performance quantum chemistry software program with strengths in life and materials sciences[7], and a modern free‐energy calculation protocol with a robust force field that enabled accurate, prospective predictions of relative ligand binding potency[8], driving predictive, transferable multiscale modeling in this era. William L. Jorgensen[2] is another leading figure in this era, with affiliations at Stanford University[5] and the University of Pennsylvania[6]. In this era, the 2015 paper Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field[8] illustrates how modern free-energy methods and robust parameterization enable cross-scale predictive binding and ADME-relevant decisions.

Graph Network Modeling era

John Z. H. Zhang [1] is a prominent figure in graph-network–driven modeling during this era, with affiliations at China Pharmaceutical University [3] and the University of Alabama at Birmingham [4]. Zhang [1] contributed to validating end-point binding free energy estimates by analyzing entropy effects in the paper Assessing the performance of MM/PBSA and MM/GBSA methods [6], a finding that guided reliable application of these methods in graph-network modeling this era. Huiyong Sun [2] is a prominent figure in this era with affiliations at Soochow University [5] and China Pharmaceutical University [3]. Sun [2] contributed to evaluating the same end-point free energy methods and elucidating entropy effects in the paper Assessing the performance of MM/PBSA and MM/GBSA methods [6], insights that supported scalable, data-driven workflows in graph-network modeling across molecules and materials.