Molecular Modeling Overview

Principles

Definition

Molecular modeling is a computational methodology that employs mathematical models and algorithms to simulate and analyze the structure, properties, and behavior of molecular systems. This approach is crucial for understanding molecular interactions, predicting physical and chemical phenomena, and guiding the design of new materials and pharmaceuticals across various scientific domains.

Ontological type

Application Areas

Core Algorithms

Modeling Workflows

Key developments

Key Figures

Empirical Force-Field Foundations era

Michael J. S. Dewar [1] is associated with the University of Chicago [3] and Queen Mary University of London [4] in this era. He contributed ground states of molecules XXV MINDO/3, an improved semiempirical SCF-MO method [7], advancing semiempirical quantum chemical modeling and enabling practical energy minimization and landscape mapping for biomolecular systems. Harold A. Scheraga [2] is associated with Harvard University [5] and the Weizmann Institute of Science [6] in this era. His key contributions include Theoretical determination of sterically allowed conformations of a polypeptide chain by a computer method [10], Conformational Analysis of Macromolecules IV Helical Structures [8], and Influence of local interactions on protein structure I Conformational energy studies of N-acetyl-N′-methylamides of pro-X and X-pro dipeptides [9], and together these works mapped conformational energy landscapes, underscored the crucial role of local interactions in folding, and provided the computational framework for energy minimization and landscape characterization that defined the era.

Solvation and Scalable Pipelines era

Martin Karplus [1] is associated with Harvard University [3] and the École Supérieure de Biotechnologie de Strasbourg [4] during this era. His <scp>CHARMM</scp>: A program for macromolecular energy, minimization, and dynamics calculations [6] and the All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins [7] advanced macromolecular energy calculations and dynamics, enabling scalable simulations that integrated with solvation concepts, while the Absolute Binding Free Energies: A Quantitative Approach for Their Calculation [8] provided quantitative frameworks for estimating binding energetics, crucial for structure-based hypotheses in this era. Peter A. Kollman [2] is linked with Harvard University [3] and the University of California, San Francisco [5] in this period. His Development and testing of a general amber force field [9], How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? [10], and An approach to computing electrostatic charges for molecules [11] anchored standardized force-field parameterization, charge derivation, and electrostatics essential for solvated modeling and docking pipelines in this era.

Validated Machine-Learned Energetics era

Alexander D. MacKerell [1] is associated with University of California, San Francisco [3] and University of Pennsylvania [4] during the Validated Machine-Learned Energetics era. His key contribution centers on optimizing the additive CHARMM All-Atom Protein Force Field through the work Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone φ, ψ and Side-Chain χ1 and χ2 Dihedral Angles [7], which improved sampling of backbone dihedrals and side-chain angles, thereby enabling more reliable, reproducible energetic predictions in this era. Robert B. Best [2] is associated with American College of Medical Genetics [5] and Baylor College of Medicine [6] in this era. His contributions align with the same paper [7], illustrating how force-field refinements and standardized benchmarking have underpinned reproducible ML/MM energetics and scalable binding-energetics predictions central to this era.