Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.

Artificial intelligence (AI) is rapidly transforming numerous aspects of daily life, including clinical practice and biomedical research. In light of this rapid transformation, and in the context of medical genetics, we assembled a group of leaders in the field to respond to the question about how AI is affecting, and especially how AI will affect, medical genetics. The authors who contributed to this collection of essays intentionally represent different areas of expertise, career stages, and geographies, and include diverse types of clinicians, computer scientists, and researchers.

Federated analysis of autosomal recessive coding variants in 29,745 developmental disorder patients from diverse populations.

Autosomal recessive coding variants are well-known causes of rare disorders. We quantified the contribution of these variants to developmental disorders in a large, ancestrally diverse cohort comprising 29,745 trios, of whom 20.4% had genetically inferred non-European ancestries.

Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation.

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang (, 2015, , 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections.

Impact of electronic polarizability on protein-functional group interactions.

Interactions of proteins with functional groups are key to their biological functions, making it essential that they be accurately modeled. To investigate the impact of the inclusion of explicit treatment of electronic polarizability in force fields on protein-functional group interactions, the additive CHARMM and Drude polarizable force field are compared in the context of the Site-Identification by Ligand Competitive Saturation (SILCS) simulation methodology from which functional group interaction patterns with five proteins for which experimental binding affinities of multiple ligands are available, were obtained. The explicit treatment of polarizability produces significant differences in the functional group interactions in the ligand binding sites including overall enhanced binding of functional groups to the proteins.

Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization.

Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein.

Development and Application of a Coarse-Grained Model for PNIPAM by Iterative Boltzmann Inversion and Its Combination with Lattice Boltzmann Hydrodynamics.

The polymer poly(N-isopropylacrylamide) (PNIPAM) is studied using a novel combination of multiscale modeling methodologies. We develop an iterative Boltzmann inversion potential of concentrated PNIPAM solutions and combine it with lattice Boltzmann as a Navier-Stokes equation solver for the solvent. We study in detail the influence of the methodology on statics and dynamics of the system.